thunderstorm algorithm

The installation of distributed generation (DG) units in power distribution networks is becoming more prominent. The impact of DG units on the voltage qualitative index has become significant day by day. Optimization techniques are tools which can be used to locate the DG units in the system, so as to utilize these units optimally within certain limits and constraints. Thus, the impacts of DG unit's issues, such as voltage qualitative index and voltage profile, can be analyzed effectively. The ultimate goal of this paper is to propose a method of locating DG units so as to improve the voltage qualitative index. The load and renewable DG generation of only active power supply with probabilistic nature are considered in this study. The proposed method starts by selecting candidate buses into which to install the DG units on the system, prioritizing buses which are sensitive to voltage profile and thus improve the voltage qualitative index. The DG units' placement is formulated using nonlinear programming method with an objective function of improving the voltage qualitative index corresponding to then individual bus voltage enhancement where the constraints are the system voltage limits, current limits, systems load demand and the DG penetration level.

Economic load dispatch (ELD) is the main optimization task in power system operation. Minimizing the fuel cost by optimally setting the real power outputs from generators is the objective of ELD problem. In this work, ELD problem is addressed by considering three different cost functions. Real power generations are adjusted for minimizing the fuel cost by using flower pollination algorithm (FOA). This algorithm works on the basis of pollinating behavior of flowering plants. Unlike the other nature inspired algorithms, it follows only the levy flight mechanism for generating the population for the next generation. Being free from large number of parameters, the algorithm works well and there is no much difficulty in tuning to suit for different problems. The algorithm can be coded easily in any programming language. The proposed algorithm is tested on the standard IEEE-30 bus system and the results are compared with those of the other algorithms reported in the literature. The result...

In Iranian electricity tariff regulations, two tariff seasons, namely warm and non-warm seasons, to allow the government to increase the energy price in warm days as 20%. This is a tariff based demand side management policy. However, the mismatch between these seasons and the reality of power system operating condition decreases this policy efficiency. So, a new approach is proposed in this paper to set these seasons online and dynamically. To this end, the days of a year are clustered independently in two groups, using K-means algorithm, once based on the daily average temperature and again based on the daily peak load. Afterward, the seasonal membership situation of a day is set based on the comparison between obtained results and the operating conditions of the power system which can be affected by the weather, accidents, and technical network limitations. To analyze the performance of the proposed approach, an industrial consumer bill is calculated with respect to the new tariff seasons and compared with the consumer's current bill. The obtained results show that the current three-time tariff has a demand response potential which depends on the dynamical definition of tariff seasons.

Congestion, signify a crisis where limit of transmission capacity overflows than transmission network capabilities, resulting in a violation of network security limits, being thermal, voltage stability limits, or levelled crucial contingency condition. Power Distribution system comprises huge quantity of physical utilities and interconnected network make it vulnerable to congestion at any non-specific location. This paper presents the various methods to reduce the congestion in Indian power sector. Further, on the basis of limitation which has been indulge with the present scenario of power market some suggestion has been provided which may lead a convention grid to smart grid system. Keyword-Congestion; Transmission Capacity; Congestion Management; Market splitting. I. INTRODUCTION Congestion, signify a crisis where limit of transmission capacity overflows than transmission network capabilities, resulting in a violation of network security limits, being thermal, voltage, stability limits, or levelled crucial contingency condition. Power Distribution system comprises huge quantity of physical utilities and interconnected network make it vulnerable to congestion at any non-specific location [1]. A vertically integrated utility structure have a centrally directed or controlled over generation, transmission, and distribution. The optimal dispatch solution implementing security constrained to attain least cost operation of the system limits the probability of congestion and regulate power flow limits on the transmission lines [2]. The Introduction of power market and conversion of monopoly to multi-seller/multi-buyer exchange via Electricity Act'2003 make it prone to congestion and lead to congestion management. In deregulated power environment, buyer only objective of getting cheapest seller creates the specific transmission corridor overloaded and unable to execute all transaction and hence, cause congestion [3]. Congestion can have cause as temporary bottlenecks which is resultant of outages for maintenance work, technical faults or particular market conditions and Structural bottlenecks are a resultant of the level of expansion of the grid and the localization of generation and consumption within the grid comes around longer periods of time or at regular intervals. Congestion management is a mechanism to prioritize the transactions and commit to such a schedule which would not overload the network. Despite these measures, congestion can still occur in real time following a forced outage of transmission line [4]. The system operator then handles this situation by means of real time congestion management. Congestion management methods based on identification of congestion cause, efficient and maximum usability as well stability of system with economic, transparency and competitive operation. This paper illustrate the congestion management methods briefly and the role of splitting mechanism in energy exchange with real time data and comments on its results.

The generation of fractals using iterative procedures is the emerging and interesting field of research. The aim of this paper is to obtain some fixed point results in the generation of fractals using SP-iterative process with s-convexity for quadratic, cubic and higher degree polynomials. Our results extend and improve the corresponding recent results proved by other authors in the literature.

This study introduces the development of a sub meter at the Electrical Measurement Lab of BMS College of Engineering in Bengaluru India at a location that, constitutes one of the several nodes on a distribution feeder in the first floor of a seven storey building. The system, monitors the behaviour of various electrical parameters during the operation of different types of loads connected to the three phases of the node under consideration. The three phase voltages and currents are sensed in real time and by the use of efficient algorithms usingTMS320F28335 Delfino by Texas Instruments, evaluation of several electrical parameters at a sampling frequency of 20 KHz has been accomplished. An effective strategy using Arduino platform for dynamic data transmission to cloud storage and a web-based Graphic User Interface brings in the graphical visualization of the operational status of the loads and the consumption cost from any remote location. Several such units are planned at various nodes and the data of each would aggregate to measure the total consumption at the secondary of the distribution transformer feeding the building. A prototype of one such typical unit developed is presented here. Keyword-Sub-metering, Online Monitoring, GSM, Bulk Consumers I. INTRODUCTION Distribution companies provide electricity at different rates to several categories of consumers. Every state in India has different categories that cater to the needs of the business variety prevalent in their states. The common categories are domestic(residential),commercial(shops and offices) and industrial(manufacturing units) with rates being the lowest for residential consumers and highest for industrial consumers. There are separate rates for LT (Low tension) and HT(High Tension) categories. In India, LT supply is of 400 Volts for three-phase connection and 230 Volts for single-phase connection. High tension or HT supply is applicable for bulk power purchasers who need 11 kilo-Volts or above. Most bulk consumers (HT) are charged based on a two-part tariff as given by expression (1). Energy charges = Contracted demand rate p * Maximum demand + TOD p t dt + TOD p t dt +

Cloud computing is a ubiquitous platform that can be used to access services and resources. The requirements for accessing a cloud resource is minimal, however, the users of a cloud platform are initially required to configure the type of services they need to access in the cloud. Even though cloud platform is elastic, frequent upscaling is costly. This paper presents an effective technique that can be used to automatically identify user's requirements and to allocate appropriate packages depending on the requirements. Usage logs of the client is grouped geographically and group based requirements are identified. This helps to determine the resource requirement for each group which address the problem of underutilization or overutilization of resources. These requirements are passed to PSO, along with the packages offered by multiple cloud operators in the same region to identify the best package for the current requirement. Experiments conducted on this architecture proves the effective working nature of the system in minimal time and with low QoS difference. Keyword-MCDM, cloud package selection, Optimization, PSO, multi-cloud I. INTRODUCTION Adopters for cloud platform have grown enormously due to the increase in the requirements for automation of systems. However, satisfying them has prevailed to be a controversial issue. This is due to the inappropriate selection of resources, leading to either underutilization or overutilization of resources. Cloud computing is a distributed computing technique that presents the user with an infinite pool of resources that can be accessed with minimum requirements [1]. Cloud resources are not available as such for the users to access them. Instead, packages are provided to the user with varied configurations such that the users can select a configuration of their choice and the resources are assigned accordingly [2]. Packages are usually segregated as high performance and high storage, moderate performance and high storage, low performance and low storage etc. Several such combinations are provided to the user with varying levels of quality parameters. The quality parameters include performance, storage, reliability, robustness, availability etc. These packages are defined by the cloud service providers and are to be used as such, without any modifications [3]. The process of package selection plays a vital role in determining the satisfactory level of the user. The major difficulty faced during this stage is the identification of the appropriate package that suits the current requirements of the user [4]. This problem can actually be branched into two sub-problems namely; identifying the current requirements of the user and selecting the appropriate package that suits the user's requirements. Both these issues are to be performed manually. This acts as the major drawback of the selection process. Automation of these two processes has not been considered by the cloud providers, hence cloud users who are technically not well versed face this issue to a large extent. Inability of a user to identify their service requirements leads to selection of inappropriate packages and this in-turn leads to dissatisfaction. In-order to overcome the issue of dynamic service requirement peaks, cloud servers incorporate the concept of elasticity by scaling up resources as and when required [5] [6]. The scaling process is automatically done, hence the user need not worry about the sudden increase in requirements. However, these automatic process scaling tends to be costly [7]. If these resource scaling spells are rare, they need not be bothered. However if they are frequent, a package change is usually recommended as obtaining a package with higher requirement is economical compared to frequent resource scaling [8]. As of now, identifying the appropriate package is a trial and error process, which starts with an educated guess that follows with the usage levels. However determining it automatically will lead to improved reliability of the cloud services [9]. This paper proposes a requirement identification and a package selection technique that can be used to dynamically identify the user's requirements and recommend appropriate packages corresponding to the requirements.

Work zone on the execution of road maintenance projects always has negative impacts such as reduced traffic performance. The excesses of the decline in traffic performance are loss of fuel consumption of in motor vehicles. Studies minimize loss of fuel during the execution of a road improvement project in Indonesia needs to be done. Case study is the execution of National road improvement of two-lane two-way, urban and interurban road type in the Bali Provincial of fiscal year 2015. Analysis of the traffic performance using the Indonesian Highway Capacity Manual 1997. Analysis of fuel consumption using guidelines Pd.T-15-2005-B. Loss of fuel of the daytime, hourly time relationship was analysed with statistical methods. The average of loss of fuel for seven link roads obtained is 176 litres day per km. Model curves obtained is the polynomial three of the degree. Work hours of execution of road maintenance projects that pose minimal loss of fuel is at night working time between 11 PM to 6 AM. Keyword-Road maintenance, Loss of fuel, Minimize, Working. I. INTRODUCTION Road infrastructure in order to serve the road users' condition for always steady. In order to maintain steady maintenance condition. The type of road maintenance handling depends on the level of road damage. The phenomenon during the execution of road maintenance is always a negative impact for road users and the surrounding environment. This impact occurs as part of the road lane road is used in the work zone. This working zone is used as a work space during road maintenance execution [1], [13]. This work zone resulted in traffic disruption such as delayed travel, congestion and also accidents [2]-[5], [17]. Research on the negative impacts of work zones during the execution of road projects generally takes into road user cost [1], [6]. The loss of road users consists of losses due to increased vehicle operating costs (VOC) and increased time value losses. The dominant component of VOC of the dominant road users is due to the increase of fuel oil consumption [2], [4], [5], [7]. In the implementation of a two-lane two lane National Road Reconstruction maintenance project in Bali province in 2013, it was found that the loss of fuel energy reached 145 litres /day/link road [5]. This paper specifically discusses the effort to minimize loss of fuel energy consumption during the execution of the National road maintenance project in Bali province of Indonesia. II. MATERIALS AND METHODS The execution of road maintenance, especially the improvement of two lane, two-way National Road in the province of Bali in the 2015 fiscal year is used as a case study. Descriptive methods are used to describe traffic characteristics and traffic regulation patterns during road maintenance execution [8], [14]. Stages of discussion of the research are presented in the form of diagrams in Figure 1.

Recently, many algorithms have been presented for new approaches based on natural inspirations as introduced in this paper is explored from the migrating Salmon mechanism. This approach is Artificial Salmon Tracking Algorithm (ASTA) which is tested on a standard system model for carrying out a dynamic economic dispatch. In these studies, ASTA is also used to optimize the system and to get an optimal portion of the balanced combination. The results show that the solution is produced dynamically to make the optimal operation for 24 hours. The system model can be balanced based on the quadratic model while ASTA has been demonstrated clearly to search for optimal solutions.

Since environmental requirements have penetrated the power system, its operation needs to consider the fossil burning together with reducing running charges of the fuel consumption.Technically, atmospheric emissions should be controlled economically at thermal power plants for 24 hours associated with a dynamic economic dispatch (DED). Thunderstorm algorithm (TA) is used to solve the DED problem considered an over rate emission coefficient applied to IEEE-62 bus system as a tested model. Moreover, this work also uses technical and environmental requirements of the operation. Simulation results show that TA can be used to obtain the optimal solution. The computational speed is performed in a smooth characteristic while searching the solution of the DED, which is carried out in hourly results. Keyword-Dynamic Dispatch, Emission Coefficient, Power System, Thunderstorm Algorithm I. INTRODUCTION In general, power system operations are imposed in generation sections; transmission sections; distribution sections; and load sections, for conveying energy from generating sites to usage areas under a certain condition. Technically, these systems are operated using various strategies to exist generating units and to control power delivery processes. In addition, these processes are also operated to produce energy within a suitable power sharing for each power plant. In detail, the operation is conducted to technical and environmental constraints while distributing power outputs to load centres. Focused on these limitations, the environmental requirement is included in the operation especially at thermal power plants for reducing pollutants [1]-[4]. Moreover, pollutants are measured in an emission standard for decreasing and controlling various gaseous materials in air, such as CO; CO 2 ; SO x ; and NO x [4]-[9]. In particular, technical limitations are also used to put desired power outputs in a scheduled permitted portion at generating units and power transfers at the lines in order to control and monitor the operation. In fact, the existed power system spends a certain budget for the energy production and infrastructure maintenance associated with generation sections; transmission sections; distribution sections. Financially, power plants take a role for this contribution spent in the fuel procurement at the all period time operation. Recently, it becomes one of the most important problems in the power system operation to reduce the technical cost through unit commitments. By considering this condition, the cost optimization is a significant case to obtain the best schedule and to meet the load demand under operational limitations belonged to equality and inequality constraints for the 24 hours operation [1]-[5], [8], [10]-[15]. To treat the whole operation for 24 hours, the power system operation becomes complex problems managed using economical cost strategies to provide electric energy and to match operational limitations. These strategies are used to select a better decision considered the minimum total operating cost as an economic load dispatch (ELD). Many studies have been reported concerned in the optimal operating cost based on the ELD[1], [7]-[8], [11]-[12], [14], [16]-[19]. Nowadays, the economic operation becomes more dynamic and interested issues to assess hourly performances related to demand changes under various themes; constraints; and requirements. In particular, it needs to plan economically based on complexity problems considered numerous limitations with the environmental penetration [5]-[7], [13], [20]-[23]. Practically, the load demand is growth from present hour to next hour during the operation. It will give an impact to power plants for producing energy at the whole operation. Regarding this behaviour, it becomes a crucial case to decide generation sites based on the balance of users and producers. By considering the power balance, the ELD is expanded to dynamic problems for 24 hours as a dynamic economic dispatch (DED) which is oriented to decrease the operating cost and pollutant production at the same time [20]-[23]. Many previous works have been focused on the DED solved using intelligent computation methods. These methods are frequently used to bring out the problems. It has shifted the classical method implementations, for instance, robust heuristic method; artificial immune system; neural network; particle swarm optimization; and harvest season artificial bee colony algorithm [20], [22]-[26].

The purpose of this research is to produce Lecture Scheduling Using the Auto Generate Timetable Method that can be used effectively for lectures at Raharja Higher Education. The research problem in arranging schedule in the college are time, space and timeslots. Considering the list of hard constraints and soft constraints presented in one semester, thus no conflicts are created in allocating the schedule. Research methods used are questionnaires and fixed study cards, as well as System Development Life Cycle (SDLC) methodology and Object Oriented Analyses Design (OOAD). The tools used are UML, Xamp with Apache software support, PHP, MySql, and Yii Framework. The final result of this research is the application of scheduling arrangement and fixed study card which can be used for lectures at the scope of Raharja College to support academic administration system in the scope of Higher Education. Follow-up of this research is to develop academic information system. Index Terms-Lecture Scheduling, Auto Generate Time Table, SDLC, OOAD, Xamp I. INTRODUCTION One of the most important components of academic information systems is scheduling lectures. In college, scheduling program is one of the important things in the teaching and learning process, because all lecturer and student activities are dependent on the schedule, so it must be properly arranged and improved at the beginning of the academic year, so that it will not interfere with teaching and learning activities among lecturers and student (Ariani, Fahriza, et. al, 2011). Artificial intelligence, or AI, is the field that studies the synthesis and analysis of computational agents that act intelligently (Poole and David, et. Al., 2010). Lecturing scheduling problems include lecturers and spaces that can clash at the same time, availability of teaching time for lecturers is not fixed, space limitations, even distribution of courses in every week.Besides the obstacles that occur is on the preparation of lecture scheduling problems in terms of students, the limited number of lecturers to teach college. In addition, the availability of classes to learning activities undertaken which is the problem above is usually known as University Timetabling Problem (UTP). Based on the aspects that have been discussed above, then it is needed the process of lecture scheduling which is not too much or it minimizes the constraints. Thus, it requires optimization that can be applied in making the lecture scheduling. This optimization cannot fully produce optimal results but it is near optimal. There are several optimization methods that are often used to accomplish the scheduling which each has its own advantages (Azis, 2015). These methods include Ant Colony, Simulated Annealing, Genetic Algorithm. And the advantage of Ant Colony is perfectly applicable with minimal change to the combination optimization problem. Simulated Annealing has the advantage that is faster in solving iteration while Genetic Algorithm can be used to find various solution problem that exist in real world. Furthermore, several previous studies have made comparisons between the Genetic Algorithm and the Palgunadi Algorithm for the course scheduling (Yudihartanti, Syukur, et.al., 2015). This study has resulted in a combination of genetic algorithms and palgunadi algorithms with the resulting heuristic process improvement of the Genetic Algorithm evidenced by the improved fitness value generated and the less process time required (Siswono, 2013). In addition, Yudihartanti has analyzed the Comparative Method of Mamdani and Sugeno Method in the scheduling of subjects where there are differences in the process done eg in the process of aggregation implication and defuzzification process. In the trial process with the dataset obtained that the comparative results show that Mamdani method has a higher level of accuracy than the Sugeno method (Yudihartanti et al., 2011). In designing this lecture scheduling, the problem on timetable is quite wide. According to Gani (2004), timetable is the allocation of subject that has constraints to be placed on time space.

Electrical energy is indispensable in people's lives for the sake of survival and for life on the work. Society is now very dependent on electric energy because without the existence of electrical energy the community activity will be stalled. The loads used in electric energy operations vary widely. Electrical energy operations are severely disrupted due to unbalanced load and nonlinear loads. Unbalanced load and nonlinear loads used for the operation of electrical energy can be disturbing because they produce non-pure sinusoidal waves. THD_i (harmonics) caused by the unbalance of load and nonlinear load have an impact on electric energy losses and heat on the transformer. THD_i (harmonics) is caused by unbalance of load and nonlinear load Between Phases RN equal to 7.87%, at SN equal to 3.22%, and at TN 2,41%. After the Passive Filter design is installed THD_i between Phases becomes smaller that is at RN becomes 1.82%, SN becomes 0.82%, and in TN becomes 0.57%. THD_i (harmonics) between phases can be decreased by 76% after Passive Filter is installed. Desain Passive filters are very effective at reducing THD_i (harmonics) in buildings such as laboratories that use the load of imbalances and nonlinear loads for lab.

Work zone on the execution of road maintenance projects always has negative impacts such as reduced traffic performance. The excesses of the decline in traffic performance are loss of fuel consumption of in motor vehicles. Studies minimize loss of fuel during the execution of a road improvement project in Indonesia needs to be done. Case study is the execution of National road improvement of two-lane two-way, urban and interurban road type in the Bali Provincial of fiscal year 2015. Analysis of the traffic performance using the Indonesian Highway Capacity Manual 1997. Analysis of fuel consumption using guidelines Pd.T-15-2005-B. Loss of fuel of the daytime, hourly time relationship was analysed with statistical methods. The average of loss of fuel for seven link roads obtained is 176 litres day per km. Model curves obtained is the polynomial three of the degree. Work hours of execution of road maintenance projects that pose minimal loss of fuel is at night working time between 11 PM to 6 AM. Keyword-Road maintenance, Loss of fuel, Minimize, Working. I. INTRODUCTION Road infrastructure in order to serve the road users' condition for always steady. In order to maintain steady maintenance condition. The type of road maintenance handling depends on the level of road damage. The phenomenon during the execution of road maintenance is always a negative impact for road users and the surrounding environment. This impact occurs as part of the road lane road is used in the work zone. This working zone is used as a work space during road maintenance execution [1], [13]. This work zone resulted in traffic disruption such as delayed travel, congestion and also accidents [2]-[5], [17]. Research on the negative impacts of work zones during the execution of road projects generally takes into road user cost [1], [6]. The loss of road users consists of losses due to increased vehicle operating costs (VOC) and increased time value losses. The dominant component of VOC of the dominant road users is due to the increase of fuel oil consumption [2], [4], [5], [7]. In the implementation of a two-lane two lane National Road Reconstruction maintenance project in Bali province in 2013, it was found that the loss of fuel energy reached 145 litres /day/link road [5]. This paper specifically discusses the effort to minimize loss of fuel energy consumption during the execution of the National road maintenance project in Bali province of Indonesia. II. MATERIALS AND METHODS The execution of road maintenance, especially the improvement of two lane, two-way National Road in the province of Bali in the 2015 fiscal year is used as a case study. Descriptive methods are used to describe traffic characteristics and traffic regulation patterns during road maintenance execution [8], [14]. Stages of discussion of the research are presented in the form of diagrams in Figure 1.

A multiple stage solution methodology involving Priority List method and Particle Swarm Optimization Technique with Time Varying Acceleration Coefficients has been proposed for solving short term generation scheduling problem having ten thermal generating units and one solar generating unit. The per day revenue saving in operational cost after inclusion of 300 MW solar plant is quantified. The proposed method is found reliable after executing the simulations several times.

Particle swarm optimization (PSO) is a population-based stochastic search algorithm for searching the optimal regions from multidimensional space, inspired by the social behaviour of some animal species. However, it has its limitations such as being trapped into a local optima and having a slow rate of convergence. In this paper, a new method of creating a combination of a developed Accelerated PSO and a new modulated inertia coefficient for the velocity update has been proposed. Random term based on particle neighbourhood has been added in the position update formula, inspired by the Artificial Bee Colony (ABC) algorithm. To verify the proposed modified PSO, experiments were conducted on several benchmark optimization problems. The results show that the proposed algorithm is superior in comparison with standard PSO and accelerated PSO algorithms.

Since environmental requirements have penetrated the power system, its operation needs to consider the fossil burning together with reducing running charges of the fuel consumption.Technically, atmospheric emissions should be controlled economically at thermal power plants for 24 hours associated with a dynamic economic dispatch (DED). Thunderstorm algorithm (TA) is used to solve the DED problem considered an over rate emission coefficient applied to IEEE-62 bus system as a tested model. Moreover, this work also uses technical and environmental requirements of the operation. Simulation results show that TA can be used to obtain the optimal solution. The computational speed is performed in a smooth characteristic while searching the solution of the DED, which is carried out in hourly results. Keyword-Dynamic Dispatch, Emission Coefficient, Power System, Thunderstorm Algorithm I. INTRODUCTION In general, power system operations are imposed in generation sections; transmission sections; distribution sections; and load sections, for conveying energy from generating sites to usage areas under a certain condition. Technically, these systems are operated using various strategies to exist generating units and to control power delivery processes. In addition, these processes are also operated to produce energy within a suitable power sharing for each power plant. In detail, the operation is conducted to technical and environmental constraints while distributing power outputs to load centres. Focused on these limitations, the environmental requirement is included in the operation especially at thermal power plants for reducing pollutants [1]-[4]. Moreover, pollutants are measured in an emission standard for decreasing and controlling various gaseous materials in air, such as CO; CO 2 ; SO x ; and NO x [4]-[9]. In particular, technical limitations are also used to put desired power outputs in a scheduled permitted portion at generating units and power transfers at the lines in order to control and monitor the operation. In fact, the existed power system spends a certain budget for the energy production and infrastructure maintenance associated with generation sections; transmission sections; distribution sections. Financially, power plants take a role for this contribution spent in the fuel procurement at the all period time operation. Recently, it becomes one of the most important problems in the power system operation to reduce the technical cost through unit commitments. By considering this condition, the cost optimization is a significant case to obtain the best schedule and to meet the load demand under operational limitations belonged to equality and inequality constraints for the 24 hours operation [1]-[5], [8], [10]-[15]. To treat the whole operation for 24 hours, the power system operation becomes complex problems managed using economical cost strategies to provide electric energy and to match operational limitations. These strategies are used to select a better decision considered the minimum total operating cost as an economic load dispatch (ELD). Many studies have been reported concerned in the optimal operating cost based on the ELD[1], [7]-[8], [11]-[12], [14], [16]-[19]. Nowadays, the economic operation becomes more dynamic and interested issues to assess hourly performances related to demand changes under various themes; constraints; and requirements. In particular, it needs to plan economically based on complexity problems considered numerous limitations with the environmental penetration [5]-[7], [13], [20]-[23]. Practically, the load demand is growth from present hour to next hour during the operation. It will give an impact to power plants for producing energy at the whole operation. Regarding this behaviour, it becomes a crucial case to decide generation sites based on the balance of users and producers. By considering the power balance, the ELD is expanded to dynamic problems for 24 hours as a dynamic economic dispatch (DED) which is oriented to decrease the operating cost and pollutant production at the same time [20]-[23]. Many previous works have been focused on the DED solved using intelligent computation methods. These methods are frequently used to bring out the problems. It has shifted the classical method implementations, for instance, robust heuristic method; artificial immune system; neural network; particle swarm optimization; and harvest season artificial bee colony algorithm [20], [22]-[26].

This paper presents an application of Thunderstorm Algorithm for determining a committed power output considered cloud charges with various technical constraints and an environmental requirement. These works also implemented on IEEE-62 bus system throughout an operational economic dispatch covered for economic and emission aspects. The results obtained show that statistical and numerical performances are associated with charges. It also presents fast and stable characteristics for the searching speeds. By considering the cloud charge parameter, it contributes to performances and results of Thunderstorm Algorithm. In addition, the introduced algorithm seems strongly to be a new promising approach for defining the committed power output problem.

This paper presents an economic operation considered renewable energy which is optimized using thunderstorm algorithm. The problem is constrained by an emission standard and various technical limits implemented on the 62-bus system model. Simulations showed that the renewable energy inclusion penetrates to the unit commitment of generating units with strongly approach for the computational solution. This inclusion also affects to the individual power production in accordance to the fuel cost and pollutant discharge.

Present, the environmental protection penetrates public awareness to decrease atmospheric emissions as important efforts for increasing living quality in air from various gaseous effects. Technically, it has forced industrial sectors to control pollutant productions while operating machineries of engineering processes to keep all contaminant materials. This condition has also forced the power system operation to modify operational strategies of thermal power plants considered pollutant productions from combustions of fossil fuels for reducing emissions. This paper demonstrates new approaches for measuring pollutant penetrations embedded in the single priority function considered an emission standard, a dynamic penalty factor, and Thunderstorm Algorithm (TA). By considering the IEEE 62-bus system model, results obtained show that the hourly computation has different performances for the 24 hours operation. It also indicates that pollutant discharges are dominated gradually by higher contributors associated with scheduled power plants. The convergence speed of TA is smooth and fast for determining the optimal solution, which are ranged in 6-20 for the cycle and 1.8-3.8 s for the time consumption. Moreover, the 24 hours operation is powered totally in 60,826.10 MW for feeding the total load of 56,820.50 MW. This operation also spends in 291,870.60 $ for the fuel procurement and 83,233.70 $ for the compensation of the pollutant production during existing 19 generating units. In total, the accumulation of pollutant discharges from 19 power plants is 68,012.20 kg.

This paper introduces a novel intelligent computation adopted from a natural phenomenon entitled Thunderstorm Algorithm (TA) which is applied to optimize an economic dispatch under various technical constraints and environmental requirements. These studies used an IEEE-62 bus system as the sample model for demonstrating ability of TA while searching the optimal solution. Obtained results show that TA gives good performances for determining the optimal solution. This demonstration also describes that the optimal solution is searched in faster convergence and shorter time. This computation also performs its characteristic in smooth and stable processes for completing all steps. In detail, the optimal solution

At Present, the environmental protection penetrates public awareness to decrease atmospheric emissions as important efforts for increasing living quality in air from various gaseous effects. Technically, it has forced industrial sectors to control pollutant productions while operating machineries of engineering processes to keep all contaminant materials. This condition has also forced the power system operation to modify operational strategies of thermal power plants considered pollutant productions from combustions of fossil fuels for reducing emissions. This paper demonstrates new approaches for measuring pollutant penetrations embedded in the single priority function considered an emission standard, a dynamic penalty factor, and Thunderstorm Algorithm (TA). By considering the IEEE 62-bus system model, results obtained show that the hourly computation has different performances for the 24 hours operation. It also indicates that pollutant discharges are dominated gradually by higher contributors associated with scheduled power plants. The convergence speed of TA is smooth and fast for determining the optimal solution, which are ranged in 6-20 for the cycle and 1.8-3.8 s for the time consumption. Moreover, the 24 hours operation is powered totally in 60,826.10 MW for feeding the total load of 56,820.50 MW. This operation also spends in 291,870.60 $ for the fuel procurement and 83,233.70 $ for the compensation of the pollutant production during existing 19 generating units. In total, the accumulation of pollutant discharges from 19 power plants is 68,012.20 kg.

This study introduces the development of a sub meter at the Electrical Measurement Lab of BMS College of Engineering in Bengaluru India at a location that, constitutes one of the several nodes on a distribution feeder in the first floor of a seven storey building. The system, monitors the behaviour of various electrical parameters during the operation of different types of loads connected to the three phases of the node under consideration. The three phase voltages and currents are sensed in real time and by the use of efficient algorithms usingTMS320F28335 Delfino by Texas Instruments, evaluation of several electrical parameters at a sampling frequency of 20 KHz has been accomplished. An effective strategy using Arduino platform for dynamic data transmission to cloud storage and a web-based Graphic User Interface brings in the graphical visualization of the operational status of the loads and the consumption cost from any remote location. Several such units are planned at various nodes and the data of each would aggregate to measure the total consumption at the secondary of the distribution transformer feeding the building. A prototype of one such typical unit developed is presented here. Keyword-Sub-metering, Online Monitoring, GSM, Bulk Consumers I. INTRODUCTION Distribution companies provide electricity at different rates to several categories of consumers. Every state in India has different categories that cater to the needs of the business variety prevalent in their states. The common categories are domestic(residential),commercial(shops and offices) and industrial(manufacturing units) with rates being the lowest for residential consumers and highest for industrial consumers. There are separate rates for LT (Low tension) and HT(High Tension) categories. In India, LT supply is of 400 Volts for three-phase connection and 230 Volts for single-phase connection. High tension or HT supply is applicable for bulk power purchasers who need 11 kilo-Volts or above. Most bulk consumers (HT) are charged based on a two-part tariff as given by expression (1). Energy charges = Contracted demand rate p * Maximum demand + TOD p t dt + TOD p t dt +

The large amount of greenhouse gasses is emitted during the process of manufacturing, transporting of a building material, constructing and erection of buildings that have a very huge impact on the environment and wellbeing. Practicing Green building techniques will reduce and eliminate the impacts on the environment and human health. Sustainable green architecture philosophy and design will maintain harmony with the nature and conserve nature resources in and around the site. There are several methods in designing of sustainable buildings that including 'green' building materials from local sources, reducing HVAC loads, optimizing systems, and generate on-site water and energy harvesting. This paper overviews reduction of possible GHG produced during process of building construction. Keywords: Green Building; Greenhouse gases; environment and human health I.INTRODUCTION Climate change has caused in release of greenhouse gases into the atmosphere with a great threats to the 21 st century. Construction industry being one of the largest primary energy consumers, it makes a significant impact on energy use and degradation of environment. Construction of buildings, its operation and deconstruction consumes world's freshwater resources (15%), worlds all energy (40%), moreover it produces 30% of the world's greenhouse gas emissions . A Compound Annual Growth Rate (CAGR) of Indian construction sector 9.6% during the period 2016-2020 is forecasting by the Global Market . Indian construction sector is one of the top 10 spending nations in the world with an estimated assessment of $100 billion during 2012-17 . It is also estimated India to be 3 rd largest global construction market . As a result government, private and foreign direct and indirect investment have made India number one of the top ten spending nations on construction in the world [5]. A recent report estimates that India will be the third largest global construction market after China and USA (Fig: ).In order to improve the standard living conditions, India has to overcome the challenge of infrastructure. Eight percent of the gross domestic product (GDP) is spent on infrastructure projects by India's government. The 13 th Five year plan (2017-2022) projects have an investment of 10% of the national GDP into infrastructure which is a staggering $1 trillion [3]. In the recent times concerns towards increasing renewable energy production and reducing Greenhouse Gas has gained momentum with a backdrop of the committed governments across the globe in response to International treaties such as the Kyoto Protocol Treaty, Basel Convention and the Rio Declaration have compelled leading industrial countries to form environmental rules and regulations. World studies have acknowledged that the buildings are major attributors for all energy-related Co 2 emissions worldwide and these emissions are expected to grow to 11Gt (B2 scenario) or 15.6Gt (A1B scenario) by 2030 . Greenhouse gas emissions (GHG) from buildings primarily arise from their consumption of fossil-fuel based energy, both through the direct use of fossil fuels and use of electricity which has been generated from fossil fuels. The primary demand of the building sector is the consumption of energy which increases the CO 2 emission . Nowadays, buildings are responsible for use of more than 40 percent of the global energy and release of one-third of global GHG emissions in both developed and developing countries. Inter-governmental Panel on Climate Change (IPCC) has estimated on building-related GHG emissions are around 8.6 million metric tons CO 2 eqv in 2004 . The growth rate of carbon dioxide emissions recorded between year 1971 and 2004 through use of electricity in commercial buildings estimated as 2.5% per annum whereas for residential buildings is 1.7%. In this process, GHG emissions are also generated through construction materials, in particular, insulation materials, refrigeration and cooling systems. Generally, energy is consumed during manufacturing of building materials; transport of materials from production plants to building sites; construction and operation of the buildings; and demolition of the buildings (Fig: ). As a result, the largest responsibility lies with the building sector in reducing the greenhouse gas emission compared to the other major emitting sections.

In the deregulated power system environment the transmission loss plays an important role. Both its allocation and reduction impact a lot on economic consideration. Due to the nonlinearity nature, the allocation of transmission loss among different utilities creates a challenging task. This paper proposes a new formula for transmission loss allocation based on actual usage of line. It splits a common transmission line into as many sub-lines corresponding to the number of loads attached to it. It divides the total loss of the common line among the sub-lines in proportion to their individual power sharing. The net loss allocated to a particular load is the combination of individual loss occurring in different sub-lines which are concerned to the said load under the whole system. To apply this formula, the load flow solution of the system network along with the power flow tracing of lossy lines are computed. This paper uses Newton-Raphson based iterative technique to obtain the load flow solutions and proportional sharing method for power flow tracing. The proposed formula is applied to a six-bus case to prove its viability and subsequently used to find the loss allocation in IEEE-14 and IEEE-30 bus systems. Relative Electrical Distance (RED) concept which is based on topology of the system and readjustment of generations has been utilized to reduce the loss in both the systems. The amounts of loss reduction in both the cases are found to be significant. Again the loss allocations of the two systems are worked out using the proposed formula and finally comparison of loss allocations before and after the loss reduction have been presented.

In this paper, a new comparative approach has been proposed for reliable controller design. Scientists and engineers are often confronted with the analysis, design, and synthesis of real-life problems. The first step in such studies is the development of a 'mathematical model' which can be considered as a substitute for the real problem. The mathematical model is used here as a plant. Fractional integrals and derivatives have found wide application in the control of dynamical systems when the controlled system and the controller are described by a set of fractional order differential equations. Here the stability of fractional order system is checked at the different level and it is found that the stability region is large in the complex plane. This large stability region provides the more flexibility for system implementation in the control engineering. Generally, an analytically or experimentally approaches are used for designing the controller. If a fractional order controller design approach used for a given plant then the controlled parameter gives the better result.

In real world applications, optimization is an inevitable stage in any engineering design. In recent days the optimization theory is also fused into other sciences which require precision in its final result. This topic sounds like a promising domain for research almost in all areas of science and technology. Perhaps several solution methods are proposed for solving problems that require optimization algorithms, in that also the algorithms inspired by natural selection are dominant among them. This paper proposes a hybrid algorithm that integrates two well established methods, one the genetic algorithm (GA) and the other the particle swarm optimization (PSO) algorithm. Here the GA will be the main optimizer and the PSO will be used to guide the GA to locate optimal solutions quickly and effectively. Several benchmark test problems are solved and the applicability of the proposed hybrid algorithm is well established. Keyword-Genetic Algorithm, Particle Swarm Optimization, Soft Computing, Benchmark Problems I. INTRODUCTION In recent days, real world optimization problems any engineering design is a real challenge. Similarly, the optimization theory is also under pining into other sciences which requires the application of cost based design. This topic emerges as a promising topic for research almost in almost diverse fields of science and technology. As said earlier, several solution methods are developed for solving optimization problems, among them the algorithms inspired by natural selection are dominant [1]. In this introduction section, a brief review of literature will be reviewed basically with an intention only on hybrid algorithms that integrates two well established methods, one the genetic algorithm (GA) and the other the particle swarm optimization (PSO) algorithm. In [1], a new evolutionary learning algorithm is based on a hybrid of genetic algorithm (GA) and particle swarm optimization (PSO) called HGAPSO is proposed. Here individuals in a new generation are created, not only by crossover and mutation operation as in GA, but also by PSO. In another research is [2], a hybrid method combining genetic algorithms (GA) and particle swarm optimization (PSO), for the global optimization of multimodal functions is developed. [3] proposes a solution improvement phase can be assisted by knowledge stored within the parent solutions, effectively allowing parents to teach their offspring how to improve their fitness. In this paper, the evolution of each individual of the total population, which consists of the parents and the offspring, is realized with the use of a Particle Swarm Optimizer. Subsequently, a supply chain management with bi-level linear programming to supply chain distribution problem was solved using hybrid of genetic algorithm (GA) and particle swarm optimization (PSO) [4]. In another work, [5], a hybrid Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) method is used for gene selection, and Support Vector Machine (SVM) is adopted as the classifier. In [6], a novel PSO-GA-based hybrid algorithm with "dying probability" for the individuals and the "war/disease process" for the population. [7] introduces, Hybrid Particle Swarm Optimization and Genetic Algorithm (HPSOGA) is proposed to solve the multi-UAV formation reconfiguration problem, which is modeled as a parameter optimization problem. In [8] an evolutionary-based clustering algorithm based on a hybrid of genetic algorithm (GA) and particle swarm optimization algorithm (PSOA) for order clustering in order to reduce surface mount technology (SMT) setup time. Also in [9], novel combined genetic algorithm (GA)/particle swarm optimization (PSO) is presented for optimal location and sizing of DG on distribution systems. Subsequently in [10], Adaptive Genetic Algorithm (AGA) and Particle Swarm Optimization (PSO) are implemented to get optimal schedules and storage assignments for Flexible Manufacturing Systems. Based on the above review of literature confined only to the hybridization of genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm, the functional integration proposed in this research is unique of its kind. It is demonstrated that the extensive search behaviour of GA and the exhaustive search capability of PSO are superior in searching better optimal points. In the following section we will discuss in detail the proposed functional fusion of GA and PSO.

The purpose of this research is to produce Lecture Scheduling Using the Auto Generate Timetable Method that can be used effectively for lectures at Raharja Higher Education. The research problem in arranging schedule in the college are time, space and timeslots. Considering the list of hard constraints and soft constraints presented in one semester, thus no conflicts are created in allocating the schedule. Research methods used are questionnaires and fixed study cards, as well as System Development Life Cycle (SDLC) methodology and Object Oriented Analyses Design (OOAD). The tools used are UML, Xamp with Apache software support, PHP, MySql, and Yii Framework. The final result of this research is the application of scheduling arrangement and fixed study card which can be used for lectures at the scope of Raharja College to support academic administration system in the scope of Higher Education. Follow-up of this research is to develop academic information system. Index Terms-Lecture Scheduling, Auto Generate Time Table, SDLC, OOAD, Xamp I. INTRODUCTION One of the most important components of academic information systems is scheduling lectures. In college, scheduling program is one of the important things in the teaching and learning process, because all lecturer and student activities are dependent on the schedule, so it must be properly arranged and improved at the beginning of the academic year, so that it will not interfere with teaching and learning activities among lecturers and student (Ariani, Fahriza, et. al, 2011). Artificial intelligence, or AI, is the field that studies the synthesis and analysis of computational agents that act intelligently (Poole and David, et. Al., 2010). Lecturing scheduling problems include lecturers and spaces that can clash at the same time, availability of teaching time for lecturers is not fixed, space limitations, even distribution of courses in every week.Besides the obstacles that occur is on the preparation of lecture scheduling problems in terms of students, the limited number of lecturers to teach college. In addition, the availability of classes to learning activities undertaken which is the problem above is usually known as University Timetabling Problem (UTP). Based on the aspects that have been discussed above, then it is needed the process of lecture scheduling which is not too much or it minimizes the constraints. Thus, it requires optimization that can be applied in making the lecture scheduling. This optimization cannot fully produce optimal results but it is near optimal. There are several optimization methods that are often used to accomplish the scheduling which each has its own advantages (Azis, 2015). These methods include Ant Colony, Simulated Annealing, Genetic Algorithm. And the advantage of Ant Colony is perfectly applicable with minimal change to the combination optimization problem. Simulated Annealing has the advantage that is faster in solving iteration while Genetic Algorithm can be used to find various solution problem that exist in real world. Furthermore, several previous studies have made comparisons between the Genetic Algorithm and the Palgunadi Algorithm for the course scheduling (Yudihartanti, Syukur, et.al., 2015). This study has resulted in a combination of genetic algorithms and palgunadi algorithms with the resulting heuristic process improvement of the Genetic Algorithm evidenced by the improved fitness value generated and the less process time required (Siswono, 2013). In addition, Yudihartanti has analyzed the Comparative Method of Mamdani and Sugeno Method in the scheduling of subjects where there are differences in the process done eg in the process of aggregation implication and defuzzification process. In the trial process with the dataset obtained that the comparative results show that Mamdani method has a higher level of accuracy than the Sugeno method (Yudihartanti et al., 2011). In designing this lecture scheduling, the problem on timetable is quite wide. According to Gani (2004), timetable is the allocation of subject that has constraints to be placed on time space.

In the deregulated power system environment the transmission loss plays an important role. Both its allocation and reduction impact a lot on economic consideration. Due to the nonlinearity nature, the allocation of transmission loss among different utilities creates a challenging task. This paper proposes a new formula for transmission loss allocation based on actual usage of line. It splits a common transmission line into as many sub-lines corresponding to the number of loads attached to it. It divides the total loss of the common line among the sub-lines in proportion to their individual power sharing. The net loss allocated to a particular load is the combination of individual loss occurring in different sub-lines which are concerned to the said load under the whole system. To apply this formula, the load flow solution of the system network along with the power flow tracing of lossy lines are computed. This paper uses Newton-Raphson based iterative technique to obtain the load flow solutions and proportional sharing method for power flow tracing. The proposed formula is applied to a six-bus case to prove its viability and subsequently used to find the loss allocation in IEEE-14 and IEEE-30 bus systems. Relative Electrical Distance (RED) concept which is based on topology of the system and readjustment of generations has been utilized to reduce the loss in both the systems. The amounts of loss reduction in both the cases are found to be significant. Again the loss allocations of the two systems are worked out using the proposed formula and finally comparison of loss allocations before and after the loss reduction have been presented.

The work in this paper proposes a distinctive approach in Energy Management Scenario using PNN which is used as a classifier to determine the vulnerable point of the given network with Pattern Recognition strategy which minimizes the time taken to train the input data set without devastating the accuracy of the classification results. Further, a Radial Basis Function (RBF) has been implemented to provide information regarding the operating states of the power system network which are categorised as "secure state", "intermediary state" and "critical state". A voltage stability indicator UVSI was also chosen to heighten the classification accuracy of PNN. The proposed technique is tested on IEEE 57-bus system. Keyword-Voltage stability, voltage collapse, classifier, Probabilistic Neural Network (PNN), Unified Voltage Stability Indicator I. INTRODUCTION Modern power systems nowadays are forced to operate under stressed condition due to escalating increase in demand and ambitious business scenario. Soaring demands for electric power has resulted in increasingly complex interconnected systems which has become miscellaneous in deregulation framework. Stressed power transmission network is always liable to voltage collapse. This has given rise to the severe need of voltage stability analysis for accurate prediction of how vulnerable a current power system network is, which includes the identification of buses in a power system network which are most susceptible to voltage collapse. The most commonly used techniques available are based on load flow analysis, bifurcation technique [1]-[2], conventional QV, PV curves, PQ planes[3]-[6], optimization techniques in reactive margin calculation for assessment of voltage stability margin of power system networks [7]-[9]. Singularity of Jacobian and Modal Analysis (Eigen values) are undertaken as powerful tools to identify the critical points and the most sensitive elements of a network in terms of voltage stability [10]-[11]. The weaker areas of a practical system and the ranking of transmission lines with overloading are discussed in [12]. In recent years the use of Artificial Neural Networks (ANN) as a tool for voltage stability assessment has acquired a lot of interest among power system researchers [13]-[15] because of its capability to do parallel data processing with fast response and high accuracy. This paper emphasizes on the performance comparison of Unified Voltage Stability Indicator [16] in which Probabilistic Neural Network along with Radial Basis Function (RBF) has been manipulated for fast detection of UVSI result for any unseen loading of the system in IEEE 57-bus system [17]-[20]. The present work is also used to establish the various voltage stability states in a power system network which are classified into three predetermined categories. Henceforth, the three operating states of the given network are categorised as "secure state", "intermediary state" and "critical state". The state classification function has been done with the help of UVSI which is used as the supervising tool. The outcome of the present work on IEEE 57-bus network are more satisfactory and encouraging on the basis of classification perfection, validity and authenticity considering the various contingencies which includes different line and generator outages compared to [21] where the authors proposed an ANN approach with various contingencies and classified into two operating states and the results showed considerable error.

In recent years, remanufacturing, recycling and disposal of product after its end of life has been increasing attention of companies not only due to environmental regulations but it is great profitable in terms of cost. In this paper, a mathematical model was developed considering production of the products with remanufacturing facility for used products, collection/disassembly and disposal facility units for recovery of used products from the customer domain. The manufacturer serves a stationary demand by producing new items of product as well as by remanufacturing the collected used items of product or disposes them after analysis of used products at collection/disassembly units. The return rate of used item is formulated as a demand like function of purchasing price and accepted quality level of used products. In this work, particle swarm optimization is proposed to solve the model and validated with a cased study. The numerical results showed that a mixed strategy of pure production and remanufacturing is optimal with scope of environment regulations. Keyword-Used products, Remanufacturing, Disposal, Particle swarm optimization I. INTRODUCTION Rapid industrialization causes pollution and global warming which have adverse effects on the environment and this creates a need to utilize the resources to the fullest in a proper manner. The activities of the firm should not be hazardous to others. That is the firm to be eco-friendly and to meet all the norms which are laid down by the government. One of the best ways to meet the above specified requirements is that the firm should utilize the used products returned by the customers. This method in fact reduces the cost of manufacturing while being eco-friendly. The basic idea behind this paper is to develop an inventory and distribution model in supply chain network considering used products with their return ratio for remanufacturing or disposal. The work related to considering environmental criteria in supply chain decision process as a case study in [1] and the relationship between environmental uncertainty and flexibility in supply chain with performance in a manufacturing company [2] are indicating the importance of used/returned products in the supply chain. A study is carried on generic stochastic model for the design of networks comprising both supply and return channels, which was organized in a closed loop system [3]. Single echelon forward network combined with two echelon reverse network is considered in the study. A work on assessing performance and uncertainty in developing carpet reverse logistics systems is presented in [4]. The reverse supply chain considering design and environmental factors is simulated in the work. Various network design parameters are analysed for better recycling technologies and optimizing growth and possible management options.A work on production planning of a hybrid manufacturing-remanufacturing system under uncertainty within a closed-loop supply chain is presented in [5]. The work dealt with production planning and control of a single product involving manufacturing and remanufacturing operations within a closed loop reverse logistic network with machines subject to random failures and repairs. A work on the generic model of reverse logistics network design considering both repairing and remanufacturing options is presented in [6]. Branch and bound technique applied for developed mixed integer formulation in the work. The similar work is carried out, but stochastic environment is considered [7]. Fuzzy optimization method is proposed to the inventory control and production planning problem of reverse logistics with remanufacturing and disposal recovery routes [8]. An information and communication management model is proposed for electronic components in supply chain with the option of the dispose of electronic waste and developed a WEB portal for the application purpose. Managerial implications of economic value and regulation of closed loop supply chain with correlated demands and returns are presented in [9]. The study on the single product production and inventory system with remanufacturing/reuse of return products and focusing on the rate of return products as a demand function of purchasing price and quality level is presented in [10].The deterministic and stochastic models for two-echelon suppy chain with demands and returns of products under cost structures are developed and numerical analysis carried out in [11].

The increase in CO 2 production by the automotive sector has led to an increase in work towards new technologies that reduce their contribution to pollution, such as electric cars, hybrids and the use of fuels obtained from renewable sources or biofuels. This study has focused on the production of biofuels because it is the first renewable option for cleaner combustion because it significantly reduces carbon emissions. Because of this, the PES analysis looked at the reasons why this large difference occurs. The first review was of state regulations governing biofuel production in the three countries studied. The USA and People's R China have clear policies and very consistent goals accompanied by investments and tax extensions and these policies were compared with those implemented by Colombia to have a measure of how far this country is from the world powers in terms of biofuel production.

In this study the static analysis of a single stage hydraulic cylinder is considered. A complete theoretical model is developed that allows the consideration of all the factors that affect the statics of a hydraulic cylinder in its action as a compression member. The scheme is based on the classical Euler-Bernoulli beam theory, and makes detailed account of all the loadings present, and of the various types of boundary conditions.

Electricity demand in the current days is more compared to the power generated, the scarcity of power requirement is more for the day to day needs. In this paper, the modeling and simulation of a Permanent Magnet Synchronous Generator (PMSG) based wind power generation system under power system dynamic conditions are presented and also shows a simple method for converting wind energy and solar power hybrid system for AC/DC Micro-Grid application is introduced. Wind driven generator is coupled to tail vane by that energy is generated in all the directions which increases the total power and improves the efficiency of the system. A 3 bridge rectifier is used to supply AC micro-grid and Buck-Boost DC-DC converter is used to supply the DC micro-grid. This algorithm is implemented using simple microcontroller to generate gate pulses to the converters and track the power generation and consumption. A simple control algorithm is used to supply power for the DC/AC micro-grid from the small/large scale power. The simulation is supported by using MATLAB/SIMULINK tool under different grid and load conditions and the corresponding simulated results are encouraged. The working model of the proposed methodology has been implemented with hardware and results are shown.

The density current is the current caused by imposing gravity on the density difference of two fluids. In case this current enters into another fluid with a lower density, it becomes a subsurface density current. Density currents are generally classified into two groups of permanent stable currents (such as salty density current) and unstable density currents (such as sedimentary current or turbidity currents). The occurrence of this kind of currents in reservoirs of dams leads to the transfer of sediments to the dam body which creates a hazard for the facilities and structures intended to meet the dam's objectives. One of these methods is to remove the sediments of flooding currents through dynamic density current. On the other hand, it is time-consuming and costly to make a physical model for investigating the dynamic density current. Therefore, in this research, investigating the dynamic density current was conductedthrough Flow-3d software. Given that the software has six turbulence of K-e (two-equation), Laminar, Rng, One-equation, Eddy and Prandtl models. To examine the variation of the effect of slope, discharge and concentration on the frontal speed, the body speed profile, as well as the body of water entrainment, was performed for each of the models (for a total of six models, 90 runs) of 15 tests in Flow-3d software. Then, the results of the turbulence models were compared to experimental results to obtain a more accurate disturbance model. The results indicated that laminar and eddy models had the most coordination with experimental data, and the laminar disturbance model was preferable to eddy model.

The density current is the current caused by imposing gravity on the density difference of two fluids. In case this current enters into another fluid with a lower density, it becomes a subsurface density current. Density currents are generally classified into two groups of permanent stable currents (such as salty density current) and unstable density currents (such as sedimentary current or turbidity currents). The occurrence of this kind of currents in reservoirs of dams leads to the transfer of sediments to the dam body which creates a hazard for the facilities and structures intended to meet the dam's objectives. One of these methods is to remove the sediments of flooding currents through dynamic density current. On the other hand, it is time-consuming and costly to make a physical model for investigating the dynamic density current. Therefore, in this research, investigating the dynamic density current was conductedthrough Flow-3d software. Given that the software has six turbulence of K-e (two-equation), Laminar, Rng, One-equation, Eddy and Prandtl models. To examine the variation of the effect of slope, discharge and concentration on the frontal speed, the body speed profile, as well as the body of water entrainment, was performed for each of the models (for a total of six models, 90 runs) of 15 tests in Flow-3d software. Then, the results of the turbulence models were compared to experimental results to obtain a more accurate disturbance model. The results indicated that laminar and eddy models had the most coordination with experimental data, and the laminar disturbance model was preferable to eddy model.

The demand for cloud computing resources is increasing due to its accessibility everywhere at any time. When the number of clients for cloud services increases, the load on the cloud nodes becomes high. This status requires load balancing to evenly distribute client requests among the available Virtual Machines (VMs) in a Data Center (DC). There are different standard dynamic load balancing techniques, such as Throttled and Active Monitoring. In this paper, a Genetic Algorithm (GA) is incorporated with a throttled to improve load balancing. The improvement is achieved by enhancing the overall response time, the data center processing time, and the maximum resource utilization. Simulation results show the improved performance of the proposed method compared to the ESCE and Throttled. Keyword-Cloud Computing, Load Balancing, Genetic Algorithm, Cloud Analyst I. INTRODUCTION Cloud computing technology has seen a speedy growth in recent years. It has affected the growth in several sub-technologies, like storage, distributed networks, virtualization, participation, and connectivity. In [1, 2], a cloud is considered as a distributed system that can handle diverse resource requirements by users. The rule system of a cloud-user relationship is planned by the Service Level Agreement (SLA), which is an agreement between a user and a service provider. As indicated in[3], the physical structure and repairing system can be achieved by the cloud, not the user. This automatically decreases the total cost and increases system efficiency. As indicated in [4], cloud computing gives an easy way to hold data and files, and it includes the following features: virtualization, distributed computing, and web services. Any complex task that calls for enormous computational resources can be serviced by cloud computing using distributed resources in a decentralized style [5]. Although cloud computing has many countenances, there are obstacles as load balancing over the resources and task scheduling [6]. Task scheduling in a cloud environment is a problem of specifying tasks to an appropriate machine to finish their work. A task should be done within a given period. The cloud task scheduler restores the information from the cloud service manager about the case of available resources [7]. Therefore, the scheduling of task problem can be qualified as the method of finding out a model mapping for execution of user tasks with the aim of reaching the desired goals [8]. Algorithms of task scheduling in cloud computing can be done depending on diverse objectives such as balancing the load, minimizing waiting time, and maximizing the utilization of resources and the throughput of the full system. Therefore, an efficient task scheduling algorithm aims to balance diverse and conflicting parameters together at the same time[9]. Moreover, resources are not utilized efficiently due to the rise in the load so for that reason load balancing is required [10]. Load balancing is the approach of redistributing the whole load into separate nodes to guarantee that no node is overloaded, doing very little work, or idle [11]. Load balancing qualifies network resources for best response and performance and provides high gratifications to consumer [12]. As a result, providing effective load balancing techniques is key to the success of cloud computing [6]. Balancing became one of the necessary interest in cloud computing since it is not possible to predict the requests number that is rolled at each second. The inability to predict is due to changing the behavior of the cloud. Therefore, load balancing algorithms can be classified depending on the system state as static and dynamic [13, 14]. Dynamic load balancing works depending on the dynamic changes in the status of the node, i.e., it gathers, keeps and analyzes data about the entire system status. If some node has missed, it will not halt the work of the entire network, but it will affect the system performance. Some of the dynamic algorithms are Ant Colony Optimization (ACO), Honey Bee Foraging, Genetic Algorithm (GA) Active Monitoring, and Throttled.

This study introduces the development of a sub meter at the Electrical Measurement Lab of BMS College of Engineering in Bengaluru India at a location that, constitutes one of the several nodes on a distribution feeder in the first floor of a seven storey building. The system, monitors the behaviour of various electrical parameters during the operation of different types of loads connected to the three phases of the node under consideration. The three phase voltages and currents are sensed in real time and by the use of efficient algorithms usingTMS320F28335 Delfino by Texas Instruments, evaluation of several electrical parameters at a sampling frequency of 20 KHz has been accomplished. An effective strategy using Arduino platform for dynamic data transmission to cloud storage and a web-based Graphic User Interface brings in the graphical visualization of the operational status of the loads and the consumption cost from any remote location. Several such units are planned at various nodes and the data of each would aggregate to measure the total consumption at the secondary of the distribution transformer feeding the building. A prototype of one such typical unit developed is presented here. Keyword-Sub-metering, Online Monitoring, GSM, Bulk Consumers I. INTRODUCTION Distribution companies provide electricity at different rates to several categories of consumers. Every state in India has different categories that cater to the needs of the business variety prevalent in their states. The common categories are domestic(residential),commercial(shops and offices) and industrial(manufacturing units) with rates being the lowest for residential consumers and highest for industrial consumers. There are separate rates for LT (Low tension) and HT(High Tension) categories. In India, LT supply is of 400 Volts for three-phase connection and 230 Volts for single-phase connection. High tension or HT supply is applicable for bulk power purchasers who need 11 kilo-Volts or above. Most bulk consumers (HT) are charged based on a two-part tariff as given by expression (1). Energy charges = Contracted demand rate p * Maximum demand + TOD p t dt + TOD p t dt +

A multiple stage solution methodology involving Priority List method and Particle Swarm Optimization Technique with Time Varying Acceleration Coefficients has been proposed for solving short term generation scheduling problem having ten thermal generating units and one solar generating unit. The per day revenue saving in operational cost after inclusion of 300 MW solar plant is quantified. The proposed method is found reliable after executing the simulations several times.

Flexible Alternating Current Transmission System devices (FACTS) are power electronic components. Their fast response offers potential benefits for power system stability enhancement and allows utilities to operate their transmission systems even closer to their physical limitations, more efficiently, with improved reliability, greater stability and security than traditional mechanical switching technology. The unified Power Flow Controller (UPFC) is the most comprehensive multivariable device among the FACTS controllers. According to high importance of power flow control in transmission lines, new controllers are designed based on the Elman Recurrent Neural Network (NEWELM) and Neural Inverse Model Control (NIMC)with adaptive control. The Main purpose of this paper is to design a controller which enables a power system to track reference signals precisely and to be robust in the presence of uncertainty of system parameters and disturbances. The performances of the proposed controllers (NEWELM and NIMC) are based neural adaptive control and simulated on a two bus test system and compared with a conventional PI controller with decoupling (PI-D). The studies are performed based on well known software package MATLAB/Simulink tool box. .

The multiobjective hybrid ant colony optimization (ACO) and bees algorithm (BA) is proposed to solve multiobjective optimal placement of public fast charging station (FCS) for battery electric vehicle (BEV) on power distribution system. This new algorithm is named as MOHACOBA. It is used to minimize simultaneously both total cost associated with FCS and feeder loss on distribution network under many constraints of the power distribution system and traffic condition. The IEEE-69-bus in Tianjin Development Zone is the test system for the proposed approach with three case studies on fast charging head size. In order to verify performance of algorithm, four performance metrics (Hypervolume ratio, error ratio, spread and epsilon indicator) is applied to measure quality of Pareto optimal front of proposed method and it is compared with other multiobjective optimization methods i.e. MOPSO, NSGA II and MOACO. From the results, quality of Pareto optimal front of proposed approach is better than other methods. It can be summarized that MOHACOBA algorithm has effectiveness and robustness to obtain multiobjective optimal placement of FCS on power distribution system.

This study is about the used of a flywheel in an effort to improve the performance of waterwheel on irrigation channels. Flow in irrigation has low head differences including low coupled lies in lowland areas. Waterwheel undershot-type appropriate for low high head applications in an attempt to gain the electricity. A set of waterwheel developed has an outer diameter of 2 m with 12 pieces of curved-blade prepared from the material of aluminum. On the shaft of pully-belt of the step transmission conditioned the mass as flywheel varied 2-42 kg designed to performs concurrently with water wheel system. The height of flow water controlled by a sluice gate to find out the potential. As the results the performance of water wheel very dependent on the height of irrigation water. The mass of flywheel on the water wheel system can increase the rotation up to 215% as it produces the maximum electrical power of about 390 W. Keyword-Water wheel, Irrigation, River, Flywheel, Turbine I. INTRODUCTION Irrigation channels in the Koto Tibun Village are primarily provided for irrigating of agricultural land and for water supply of fisheries. This study is about the use of flywheel in order to improve the performance of undershot waterwheels. The flywheel is usually selected as a component for energy storage. A flywheel is useful for generating power to reduce the load on the generator when demand is instantly increasing, allowing the generator to maintain speed and frequency. The energy can be returned to the flywheel when the demand is reduced. Therefore, the flywheel is usually situated and turned on the shaft together. Many studies with using flywheel on the machine, but limited data for the water wheel such as a flywheel application on a multicrop threshing machine [1]. Based on their study found that 12% of the material cost and operational load on the shaft and bearings decreased and kinetic energy storage capacity increased. Another study, The Dual Mass Flywheel is used to damp the oscillations on the train engine in order to prevent shocks on the gearbox [2]. The comparative study shows that the Dual Mass Flywheel 5-6% more efficient than conventional flywheels in its use. Then there is an increase in power of about 7-8%. In throughout the irrigation channels might provide a large amount of potential energy as well. According to that condition, the regulation and optimizing the use of water resources for consumption, irrigation and hydroelectric power plant should be considered, like study have been conducted by Wu and Chen [3]. Yet, relevant to that an essential study in order to improve the energy production by water power plant concern with applied the combination water cycle power generating system has been studied by Liu and Packey [4]. Again, many studies have been conducted in order to investigate alternative and renewable energy resources. A study presents a description of the initial testing performed on the prototype pico hydro generator system for the purpose of investigating the performance [5]. The kinetic energy contained in the flow of water in domestic pipes is known to have the potential to generate electricity for energy storage purposes while performing routine activities such as laundry, cooking, and bathing. The water pressure and water flow in the pipe from the main tank utility, which is used for the activities of everyday life, used for small-scale hydro turbines rotate to drive a generator for generating electricity. The test results indicate that the readings are significantly convincing in terms of electrical voltage recorded since it can be calculated to be followed is this system is feasible for electrification aimed at storage of energy and showed good prospects for improvement and further research in the future. Furthermore, the development of a waterwheel has been carried out traditionally where it has fulfilled the needs of the water in an agricultural area [6]. In a region, wherein the water resources are available abundantly caused these efforts are highly recommended as a solution to energy deficiency in the rural area. Indonesia has the potential for hydropower are about 75.5 GW [7]. One of the sources of hydropower like that has available in Riau province where 250,571 hectares of land have been completed by the medium of irrigation canals.

Cloud computing is a ubiquitous platform that can be used to access services and resources. The requirements for accessing a cloud resource is minimal, however, the users of a cloud platform are initially required to configure the type of services they need to access in the cloud. Even though cloud platform is elastic, frequent upscaling is costly. This paper presents an effective technique that can be used to automatically identify user's requirements and to allocate appropriate packages depending on the requirements. Usage logs of the client is grouped geographically and group based requirements are identified. This helps to determine the resource requirement for each group which address the problem of underutilization or overutilization of resources. These requirements are passed to PSO, along with the packages offered by multiple cloud operators in the same region to identify the best package for the current requirement. Experiments conducted on this architecture proves the effective working nature of the system in minimal time and with low QoS difference. Keyword-MCDM, cloud package selection, Optimization, PSO, multi-cloud I. INTRODUCTION Adopters for cloud platform have grown enormously due to the increase in the requirements for automation of systems. However, satisfying them has prevailed to be a controversial issue. This is due to the inappropriate selection of resources, leading to either underutilization or overutilization of resources. Cloud computing is a distributed computing technique that presents the user with an infinite pool of resources that can be accessed with minimum requirements [1]. Cloud resources are not available as such for the users to access them. Instead, packages are provided to the user with varied configurations such that the users can select a configuration of their choice and the resources are assigned accordingly [2]. Packages are usually segregated as high performance and high storage, moderate performance and high storage, low performance and low storage etc. Several such combinations are provided to the user with varying levels of quality parameters. The quality parameters include performance, storage, reliability, robustness, availability etc. These packages are defined by the cloud service providers and are to be used as such, without any modifications [3]. The process of package selection plays a vital role in determining the satisfactory level of the user. The major difficulty faced during this stage is the identification of the appropriate package that suits the current requirements of the user [4]. This problem can actually be branched into two sub-problems namely; identifying the current requirements of the user and selecting the appropriate package that suits the user's requirements. Both these issues are to be performed manually. This acts as the major drawback of the selection process. Automation of these two processes has not been considered by the cloud providers, hence cloud users who are technically not well versed face this issue to a large extent. Inability of a user to identify their service requirements leads to selection of inappropriate packages and this in-turn leads to dissatisfaction. In-order to overcome the issue of dynamic service requirement peaks, cloud servers incorporate the concept of elasticity by scaling up resources as and when required [5] [6]. The scaling process is automatically done, hence the user need not worry about the sudden increase in requirements. However, these automatic process scaling tends to be costly [7]. If these resource scaling spells are rare, they need not be bothered. However if they are frequent, a package change is usually recommended as obtaining a package with higher requirement is economical compared to frequent resource scaling [8]. As of now, identifying the appropriate package is a trial and error process, which starts with an educated guess that follows with the usage levels. However determining it automatically will lead to improved reliability of the cloud services [9]. This paper proposes a requirement identification and a package selection technique that can be used to dynamically identify the user's requirements and recommend appropriate packages corresponding to the requirements.

Hospital Information System is the most fragile component of the health care system in the countries in development process. The modernization of the health care system does not integrate a rigid reflection on the installation of these tools into the hospital, which remain foreign with the strategies of these countries. This article aims at studying the case of a hospital Information system by schematizing the distribution of its components as well as its flow of information in the form of two complementary cartographies, a functional cartography and an information flow cartography, so this study's main objective is to reveal the axes of improvement of this system, by studying it, while focusing themselves on the transfer of information in the hospital institution which is the object of our study, to demonstrate this objective we have developed rich and flexible scenarios in the form of flowchart, that can also incorporate descriptions of users and their current and potential use of the information system. The formalization of the cartographies, in our case, enters in an improvement approach of the information system. Keyword-Hospital Information System1, components2, functional cartography3, information flow artography4, Information System improvement5. I. INTRODUCTION The introduction of any change poses a certain number of problems and of resistance at various levels. In the field of the information systems in health the problematic of introduction, although being common to all the countries, seem to have a peculiarity in the African context [8]. Indeed, African hospitals are differentiated from several angles relating to the cultural, political, organizational and structural context of hospital institutions. The management of these aspects constitutes obviously, an additional challenge to raise for the implementation and management of computerized information systems [3]. The Hospital Information system is inserted in the organization "hospital" in perpetual evolution; it is able, according to rules and preset procedures, of acquiring data, evaluating it, processing it by computer or organizational tools, to distribute information containing a strong added value to all the internal or external partners of the establishment, collaborating in a common work directed towards a specific goal, namely the assumption of responsibility of a patient and the re-establishment of this one [4]. To reinforce this state of mind, it appeared important to us to describe the information system to measure the insufficiencies, to point the factors must reinforce to build in the second time a factual base of knowledge able to help the decision makers, clinicians and managers [10]. In this work, we will start with a case study of a Moroccan hospital, which is a university hospital center, the purpose of this study is to define the existing HIS, to know its characteristics, and to measure its level of maturity, to chart and schematize the existing system and to propose improvements, schematically, per the returns on the questionnaires and the remarks made during our study. Lastly, the main aims of this study are, of one, to study the case of a Moroccan hospital institution of close, by implying the users in the study of the existing HIS, of two, try to propose improvements allowing to evolve the system per the organization evolutions and to the user needs. II. METHODOLOGY The organization which is the object of our study is a Moroccan hospital, and in order to study its IS, we used two questionnaires, the first, aimed at manager and responsible of IT department, was completed with the aim of defining the characteristics of the system; the second, aimed at the pilots of the HIS components, was in the form of a System Components Information Sheet (Modules). Our main objective is to study the state of the existing system, to have a sight on the level of maturity of this last, like arising the problems and difficulties pronounced during the use of the system, to propose improvements making it possible to make evolve the system according to the needs of its users and beneficiaries [7].

The Present Vehicle Spot Speed Study concentrates on measuring the speed characteristics at predetermined area under the natural conditions.The present spot speed studies are coordinated to assess the movement of rates of vehicles in a surge of activity at a particular region on a roadway. The proposed look into work goes from breaking down the modular speed of vehicles, highway outline component ,upper and lower speed restrain for regulation, planning and analysis, traffic operation, control and direction. The range, time and conditions of the study may be coordinated by its objective and expansion. In case approach paces to a merging are required, the estimations should be taken upstream of the intersection point going before vehicle deceleration for a possible stop at the intersection point. If the study requires free-stream speeds, the estimations should be taken in the midst of off-zenith times. A comparable method of reasoning should be taken after for estimations required in the midst of night conditions, wet black-top, and so forth. There are two ordinarily utilized ways to deal with gather vehicle speeds at spot areas: singular vehicle choice strategy and all-inspecting vehicle technique. The individual vehicle assurance system includes using a manual speed estimation method and is generally used for transient speed estimations. This segment focuses on the individual vehicle assurance methodology. In this present survey the average speed, variance & standard deviation, median speed, modal speed & percentile speed of various category of vehicles are determined and analyzed with the accident data of the area thereby finding the reason for the occurrence of accidents and determining the safe speed limits in the area and also providing measures for the improvement of road safety measures on national highway-1 like deciding existing movement operations and assessment of activity control gadgets, setting up roadway plan components, and measuring adequacy of activity control gadgets or movement programs, including signs and markings, activity operational changes, and speed implementation programs.

The system comprises of three interconnected power system networks based on thermal, wind and hydro power generation. The load variation in any one of the network results in frequency deviation in all the connected systems .The PI controllers have been connected separately with each system for the frequency control and the gains (Kp and Ki) of all the controllers have been optimized along with frequency bias (Bi) and speed regulation parameter (Ri). The computationally intelligent techniques like bacterial foraging optimization (BFO) and particle swarm optimization (PSO) have been applied for the tuning of controller gains along with variable parameters Bi and Ri. The gradient descent (GD) based conventional method has also been applied for optimizing the parameters Kp , Ki ,Bi and Ri .The frequency responses are obtained with all the methods. The performance index chosen is the integral square error (ISE). The settling time, peak overshoot and peak undershoot of all the frequency responses on applying three optimization techniques have been compared. It has been observed that the peak overshoot and peak undershoot significantly reduce with BFO technique followed by the PSO and GD techniques. While obtaining such optimum response the settling time is increased marginally with bacterial foraging technique due to large number of mathematical equations used for the computation in BFO. The comparison of frequency response using three techniques show the superiority of BFO over the PSO and GD techniques. The designing of the system and tuning of the parameters with three techniques has been done in MATLAB/SIMULINK environment. Keyword-Bacterial foraging optimization; Particle swarm optimization; Gradient Descent ; Integral square error; peak overshoot; peak undershoot; settling time; PI controller I. INTRODUCTION One of the power system requirement is the stable operation in respect of voltage and frequency under varying load conditions. The variation in these parameters must lie within permissible range. The real power control help in achieving the frequency control and reactive power help in achieving the voltage control. The frequency deviation occurs in all the interconnected areas when they are connected through same tie line. The performance index has to be minimized continuously whenever there is frequency change .The performance index in the present case is chosen as the integral square error (ISE) which has to be minimized whenever there is frequency change using the computationally intelligent techniques. The load frequency controller is the proportional integral (PI) controller which is the most effective controller if it is properly tuned for proportional (Kp) and integral (Ki) gains and it is used in decentralized control mode as the interconnected areas are of unequal nature. The interconnected power system areas in most of the literature are of same nature but in actual scenario this trend is changing due to the increasing scarcity of non renewable energy generation sources. The interconnected systems are of different nature and unequal ratings which are connected with a tie line. The proper tuning of regulation parameter (Ri) and frequency bias (Bi) of these power systems is also a tedious task under varying load conditions [1]. The three tie line connected systems in the present network comprises of thermal, wind and hydro power generation systems. The gains of three controllers and variable parameters of the systems are optimized by computationally intelligent techniques like BFO, PSO and as well as classical gradient descent (GD) method [2] .The effectiveness of these methods depends upon the convergence, precision and robustness of the technique. There are chances that the solution parameter gets trapped in local minima in case of GD method and

A multiple stage solution methodology involving Priority List method and Particle Swarm Optimization Technique with Time Varying Acceleration Coefficients has been proposed for solving short term generation scheduling problem having ten thermal generating units and one solar generating unit. The per day revenue saving in operational cost after inclusion of 300 MW solar plant is quantified. The proposed method is found reliable after executing the simulations several times.

In this study, optimal economic load dispatch problem (OELD) is resolved by a novel improved algorithm. The proposed modified moth swarm algorithm (MMSA), is developed by proposing two modifications on the classical moth swarm algorithm (MSA). The first modification applies an effective formula to replace an ineffective formula of the mutation technique. The second modification is to cancel the crossover technique. For proving the efficient improvements of the proposed method, different systems with discontinuous objective functions as well as complicated constraints are used. Experiment results on the investigated cases show that the proposed method can get less cost and achieve stable search ability than MSA. As compared to other previous methods, MMSA can archive equal or better results. From this view, it can give a conclusion that MMSA method can be valued as a useful method for OELD problem. Keywords: Discontinuous objective function Modified moth swarm algorithm Optimal economic load dispatch This is an open access article under the CC BY-SA license. Nomenclature Btn, B0t, B00 Coefficients of B-matrix for transmission power loss Cp The number of randomly selected control variables among Dim variables Ds Total system demand Dim The number of control variables of each solution 12 ,  , r1, r2, r3 Random numbers distributed uniformly within the interval [0,1] j The jth variable of the pth new solutions Lp1, Lp2 Two Lévy flight distributions mt, nt, ot Fuel cost function coefficients of the tth thermal generator mtS, ntS, otS Fuel cost function coefficients for the S fuel type of the tth thermal generator n1, n2, n3 The number of solutions in group 1, group 2 and group 3 tS tS PP ,min ,max , The minimum and maximum power output of the tth thermal generator corresponding to the fuel cost source S Pt Power output of the tth thermal generator TELKOMNIKA Telecommun Comput El Control  Modified moth swarm algorithm for optimal economic load dispatch problem (Thang Trung Nguyen) 2141 tt PP ,min ,max , The minimum and maximum power output of the tth thermal generator  tj tj l u PP 1 , The lower and upper limits of the jth prohibited operating zone of the tth generation unit t, T The current iteration and the maximum iteration Vr1, Vr2, Vr3, Vr4, Vr5, VrA Randomly selected solutions from solutions best Gbest V V , The best solution in group 1, group 2 and all groups 1. INTRODUCTION In a power system, electric energy is produced by thermal plants, hydropower plants and renewable power plants. The fuel cost of renewable power plants such as solar thermal plants, photovoltaic power plants and wind turbines is approximately equal to zero; however, the sources are unstable and changeable during a small interval. On the contrary, the fuel cost for power generation of thermal plants is very expensive owing to fossil fuel. In the future, the fossil fuel including gas coal, and oil becomes exhausted. So, the fuel cost of thermal plants is the main objective during the operation of the power systems. So far, a solution for dealing with the fuel cost of thermal plants can be implemented by an optimal economic load dispatch problem (OELD). The work in OELD problem is to determine the best effective strategy for allocating the power output of all available thermal plants so that total fuel cost of plants can be decreased at least level [1]. In this paper, we concentrate to study three systems that are employed to test the powerful ability of optimization tools. The first system with 6 units considers single fuel, prohibited zones and power loss. The second system with 10 units considers multiple fuels. The last system with 20 units considers power losses in the line transmission. For the first system, a huge number of methods consisting of modified particle swarm optimization (MPSO) [1], hybrid bacterial foraging algorithm and Nelder Mead algorithm (HBFNM) [2], differential evolution (DE) algorithm [3], multiple tabu search algorithm (MTS) [4], self-organizing hierarchical particle swarm optimization (SOH_PSO) [5], new adaptive particle swarm optimization (NAPSO) [6], krill herd algorithm (KHA) [7], chaotic bat method (CBM) [8], exchange market method (EMM) [9], adaptive charged system search method (ACSS) [10], opposition based krill herd method (OKHM) [11], and improved social spider optimization algorithm (ISSO) [12] have been satisfactorily applied. In this method group, MPSO [1] is a version of particle swarm optimization developed in 2007 while ISSO [12] is a variant of social spider optimization algorithm (SSO) proposed in 2019. ISSO was improved based on the classical SSO by proposing three improvements. As a result, optimal solutions found by ISSO were better than MPSO and other methods. For 10-unit system, many methods as DE [3], antlion optimization algorithm (ALO) [13], artificial immune system (AIS) method [14], enhanced augmented Lagrange Hopfield network (EALHN) [15], enhanced lagrangian artificial neural network (ELANN) [16], improved quantum-behaved particle swarm optimization (IQPSO) [17], modified firefly algorithm (MFA) [18] and modified stochastic fractal search algorithm (MSFS) [19] have been successfully employed with the impressive results. Among these methods, MSFS is the latest tool that has been formed by proposing three modifications based on the structure of stochastic fractal search (SFS). By applying these modifications, the search ability of MSFS has been significantly improved when compared to SFS in term of solution quality, convergence speed and stabilization. For the last system, several methods are used for OELD problem. They include MFA [18], MSFS [19], Hopfield model (HM) [20], biogeography-based optimization (BBO) algorithm [21], general algebraic modeling system (GAMS) [22], improved group search optimizer (IGSO) [23], backtracking search algorithm (BSA) [24] and improved cuckoo search algorithm (ICSA) [25]. The contribution of the above algorithms is worthy of recognition in dealing with such OELD problem because these algorithms supply different solutions in aim to the most economical and stable power system operation. Moth Swarm Algorithm (MSA) was a population-based method that proposed in 2017 [26]. Although MSA has used three phases including reconnaissance phase, transverse orientation phase and celestial navigation phase for producing new solution, it only has produced a number of new solutions equaling to population. The disadvantage of MSA is low solution quality, many calculation processes and variation searches by owning many formulas. In this study, a modified moth swarm algorithm (MMSA) is proposed pursuant to the traditional MSA by canceling ineffective formulas and using effective one to deal with drawbacks of MSA. Via three test systems, the results found by the proposed method are compared to other ones for solving OELD problem. Consequently, the key work considered as contributions in the study can be presented as follows:-Point out disadvantages of MSA-Suggest highly effective improvements on MSA-MMSA has a faster simulation time and reaches a high performance and enhances stable search ability

The aim of this study to predictedthe duration of road projects in republic of Iraq. Historical data was adopted for (99) projects for interval between 2000 to 2017 from Roads and Bridges Directorate (RBD). Artificial Neural Network (ANN)model used to estimate the duration usingsix variables (length of road, No.of lane, No.of intersection, volume of earth, type of pavement and furniture level). The methodology used in this study included two important parts, the first part, reviewing the literature of the subject (estimating the duration of the road projects), and the second part, used of a program neuframe v.4 to build the models of neural networks to estimate the duration of road project. The results showed strong correlation between actual duration and predict duration by (90.6%), minimizes testing error (3.2%) and training error (4.9%). The MAPE and Average Accuracy Percentage generated by ANN model were found to be(25.73 %) and(74.27%) respectively. Therefore, it can be concluded that ANNs model show very good agreement with the actual measurements. I. INTRODUCTION The estimation team gives the construction cost a big importance among the others management item (quality and time) [1]. Therefore, this study aims to highlight the process of estimating the duration in field of the project management. Project duration can only be estimated once when resources are available. The objective of efficiency the project management is to bring the project to achievement on schedule and cost. The accuracy of projects schedule depends on the accuracy of those estimations. The hallmark of the neural network is massive parallelism and high interconnectivity between a large numbers of relatively simple processors. The information in a neural processor is stored in the interconnection pattern rather than at specific spatial locations uniquely defined by a memory address.