PSO Algorithm

This work presents a comparative evaluation of adaptive particle swarm optimization (PSO) variants for the optimal placement and sizing (OPS) of photovoltaic-based distributed generation (PV-DG) and DSTATCOM units in the standard IEEE 33-bus radial distribution network (RDN). Five adaptive PSO algorithms are investigated, namely adaptive acceleration coefficients PSO (AAC-PSO), autonomous particle groups PSO (APG-PSO), nonlinear dynamic acceleration coefficients PSO (NDAC-PSO), sinecosine acceleration coefficients PSO (SCAC-PSO), and time-varying acceleration PSO (TVA-PSO). The optimization framework is structured as a single-objective problem focused on maximizing the active power loss index (APLI), which is used as a normalized indicator associated with active power loss reduction. To further assess the technical quality of the obtained solutions, two additional performance indicators are considered, namely the total voltage deviation (TVD) and the voltage stability index (VSI). The simulation outcomes indicate that the TVA-PSO algorithm exhibits superior overall performance compared to other evaluated variants in terms of convergence behavior and solution quality. In particular, it achieves the highest APLI value of 92.52%, corresponding to an active power loss reduction of 91.91%, with active power losses (APL) reduced from 210.99 kW to 17.07 kW. In addition, the obtained solution significantly improves the network voltage profile (VP) and enhances voltage stability. These findings provide evidence that the effectiveness of adaptive PSO strategies for optimizing PV-DG and DSTATCOM integration in RDN.

Brushless direct current (BLDC) motors are widely utilized in industrial and traction applications, particularly in electric vehicles (EVs), due to their inherent advantages. In EVs, a very low steady-state error with rapid convergence responses is required for a smooth wheel acceleration. This article proposes a robust and fast speed controller for the BLDC-driven EV using optimized proportional-integral (PI) and fractional-order (FOPI) controllers tuned by a new bio-inspired optimization algorithm called the Giant Armadillo Optimization (GAO) algorithm. The main goal of this study is to improve the performance of the BLDC motor under different variations of the reference speed. The suggested system was designed and tested using the MATLAB/Simulink environment. In this study, three different case studies are applied, including no load, sudden decrease in speed, and random speed variations. Moreover, the achieved results are compared with the classical PI and particle swarm optimization (PSO-PI) controllers. The obtained results show that the suggested GAO-FOPI controllers significantly outperform the conventional controllers, obtaining a speed overshoot of 2.13%, compared to 8.4% with the classical PI method. Finally, the suggested controller decreases the rise time to 1.02 sec, achieving a 41% enhancement when compared with PSO-PI and PI techniques (1.64 sec and 1.72 sec).

Optimizing the proportional-integral (PI) controllers in brushless direct current (BLDC) motors can improve performance by minimizing the rise time, settling time, and steady-state error. Therefore, a new strategy for speed control for BLDC motors was presented using the bald eagle search (BES) algorithm for optimizing the PI controller. The main objective of this research was to enhance the dynamic performance of the BLDC motor with high efficiency and low steady-state error under load saturation and different operating conditions. The novel speed controller based on the BES-PI methodology is suggested to optimize the controller parameters without the necessity of using the trial-and-error technique. Simulations of the BLDC motor governed by hysteresis current control (HCC) were conducted through the MATLAB/Simulink environment. The obtained results were compared with those achieved using particle swarm optimization (PSO), classical PI, and slide mode control (SMC). The BLDC motor-based BES algorithm showed better robustness, better accuracy for tracking reference speed, and better disturbance rejection than traditional controllers. The average rise time values of the SMC, PI, and PSO-PI techniques were 0.016 s, 0.048 s, and 0.023 s, respectively. In addition, the settling times were 0.61 s for the SMC method, 0.21 s for the PI method, and 0.012 s for the PSO-PI method. Finally, the BES-PI control was the best controller for BLDC motors, with average rise time values of 0.01 s, a settling time of 0.05 s, and no overshoot.

In the long history of science, there are two kinds of pioneers: those who build upon existing maps, and those who venture beyond the edge of every known chart to draw entirely new ones. Satish Gajawada belongs unmistakably to the second kind.

Electro-hydraulic servo systems utilizing multi-stage hydraulic cylinders (MSHCs) in erection mechanisms face strong nonlinearities, time-varying loads, and model uncertainties that challenge conventional controllers. This paper develops and compares different control strategies for such systems: the classical Proportional-Integral-Derivative controller (PID), a PID controller whose gains are tuned via Particle Swarm Optimization (PSO), fractional order PID tuned by PSO (FOPID-PSO), and a robust Sliding Mode Control (SMC) scheme with Lyapunov-based design and a boundary-layer implementation to mitigate chattering. A nonlinear model of the erection mechanism and MSHC, including inter-stage impacts captured by a Kelvin-Voigt impact model and controlled by a proportional directional control valve, is formulated and simulated using MAT-LAB/Simulink. Results show that PSO-optimized PID significantly improves tracking and settling behavior over baseline PID, FOPID-PSO adds flexibility to the PID, while robust SMC maintains accuracy and disturbance rejection under large load variations, but with a pronounced peak control signal during the stage-transition interval. The study quantifies performance, demonstrating that the control approaches enhance stability and robustness, with SMC offering the strongest resilience to uncertainties and PSO providing a simple and high-performance tuning route for implementation. The results are based on numerical simulation and serve as a baseline for future experimental validation and hardware realization.

Wearable and portable devices are gaining significant popularity across consumer electronics as well as in medical and industrial fields. To ensure that these devices are both comfortable and appealing to users, they need to have low battery consumption and be compact in both size and weight. The EGluco project is focused on developing a wearable device for noninvasive blood glucose monitoring. This multi-sensor device incorporates electrical bioimpedance spectroscopy as one of its measurement techniques. One of the earlier versions of the device was deemed unsuitable as a wearable due to its large size and high power consumption. To make the device more suitable for wearability, the previous hardware was assessed, and a new design was proposed that simplified the system's power supply and reduced the operating voltage. This article presents two of these designs: an improved Howland current source with a supply voltage of 3.3 V , an output current of 250 µA, and the ability to conduct bioimpedance analysis up to 1 MHz using pulsed DIBS (Discrete Interval Binary Sequence) signals, and an instrumentation amplifier with the same supply voltage as the current source, a voltage gain of four, and a slew rate of 150 V /µs. By simplifying the power supply and implementing other changes, the device's size was reduced to a single 5 × 5 cm circuit board, compared to the previous configuration of four separate boards connected by cables.

The satisfaction of electricity customers and environmental constraints imposed have made the trend towards renewable energies making them more essential due to their advantages as reducing power losses and ameliorating system’s voltage profiles and reliability. This article addresses the optimal location and size of multiple distributed generations (DGs) based on solar photovoltaic panels (PV) connected to electrical distribution network (EDN) using the various proposed hybrid particle swarm optimization (PSO) algorithms based on chaotic maps and adaptive acceleration coefficients. These algorithms are implemented to optimally allocate the DGs based PV (PV-DG) into EDN by minimizing the multi-objective function (MOF), which is represented as the sum of three technical parameters of the total active power loss (TAPL), total voltage deviation (TVD), and total operation time (TOT) of overcurrent relays (OCRs). The effectiveness of the proposed PSO algorithms were validated on both standards IEEE 33-bus, and 69 bus. The optimal integrating of PV-DGs into EDNs reduced the TAPL percentage by 56.94 % for the IEEE 33-bus and by 61.17 % for the IEEE 69-bus test system, enhanced the voltage profiles while minimizing the TVD by 37.35 % and by 32.27 % for two EDNs, respectively.

In this paper a robust fractional order PID (FOPID) controller is proposed to control the automatic voltage regulator (AVR) system, the tuning of the controller gains are done using whale optimization algorithm (WOA) and integral time absolute error (ITAE) cost function is adopted to achieve an efficient performance. The transient analysis was done and compared with conventional PID in terms of overshoot, settling time, rise time, and peak time to explain the superiority of the proposed controller. Finally, a robustness analysis is done by adding external disturbances to the system and changing the system parameters by ±20% from its original value, the controller overcomes the disturbances signals with less than 0.25 s and faces the changes of the system values and returning the response within (0.7-1) sec and led the system to the desired response efficiently. The numerical simulations showed that the smart WOA offers satisfying results and faster response reflected clearly on the robust and stable performance of the proposed controller in improving the transient analysis of AVR system response.

In the present work, a precise optimization method is proposed for tuning the parameters of the COST231 model to improve its accuracy in the path loss propagation prediction. The Particle Swarm Optimization is used to tune the model parameters. The predictions of the tuned model are compared with the most popular models. The performance criteria selected for the comparison of various empirical path loss models is the Root Mean Square Error (RMSE). The RMSE between the actual and predicted data are calculated for various path loss models. It turned out that the tuned COST 231 model outperforms the other studied models.

The highly interacting multi input multi output (MIMO) process, such as distillation columns, is often decoupled into a collection of separate loops, with each loop being controlled by a traditional PID controller. It is exceedingly challenging to tune typical PID controllers. This paper presented and independently implements a comparative performance analysis of several control algorithms for MIMO process. This study examines the simulation model and control of the Vinante-Luyben and Wood-Berry models utilizing Particle Swarm Optimization (PSO) based optimized PID and inverted decoupling of the distillation column. Multivariate "first-order plus time-delay" systems (distillation columns) used the proportional Relative Gain Array and the Niederlinski index, respectively, to examine stability and interaction.

This paper introduces a novel approach to controlling Brushless DC (BLDC) motors using Artificial Intelligence (AI), specifically through the Nonlinear Auto Regressive Moving Average with exogenous inputs (NARMA-L2) model. By utilizing MATLAB simulations, this method is compared to the traditional Proportional-Integral-Derivative (PID) controller, which was optimized using an optimization technique in prior research. The results demonstrate that the NARMA-L2 model provides better adaptability and efficiency, addressing the limitations of conventional control methods. The proposed AI-based approach improves speed regulation, torque response, and overall system stability, making it a more effective alternative. Additionally, this study highlights the potential of AI in solving complex control challenges in electro-mechanical systems, paving the way for further research and real-world applications in various industries.

Wireless sensor networks (WSN) are increasingly requiring directional antennas that not only provide higher capacity, security, transmission range or robustness against interference, but also contribute with smart antenna capabilities such as adaptive beamforming or multi beam radiation patterns. Standard phased arrays provide these features, but employing feeding networks based on digitally controlled variable phase shifters (VPSs) which have the disadvantage of high cost and limited angular resolution. Instead, time-modulated arrays (TMAs) use switched feeding networks governed by digital periodic sequences which allow harmonic patterns to be radiated and endows (TMAs) with attractive multifunctional capabilities. In this paper, we analyze and properly combine (TMA) switched feeding networks capable of time-modulating an antenna array with discretized amplitude modulation (AM) and phase modulation (PM) waveforms. The advantages of the proposed innovative dual-beam (TMA) with respe...

This paper presents a detailed study of developing a mathematical model and experimental analysis of electric drive processes in belt conveyors. The proposed model simplifies the complex real mechanical system by substituting distributed parameters, such as the transported load's mass and the traction element's elasticity, with concentrated equivalents. A comprehensive investigation of key transient processes including stator currents speed, torque and resistance forces was performed using MATLAB's Simulink environment. The findings reveal significant differences in performance between the initial startup phase and operation under loaded conditions. To validate the model's accuracy, the authors employed numerical analyses utilizing regression metrics such as root mean square error (RMSE) and correlation coefficients. The results show that the proposed model significantly outperforms similar models in the literature with a notable RMSE of 12.5 A for stator current, reflecting an 18% improvement and 8.7 Nm for torque prediction, indicating a 15% enhancement. Furthermore, the model achieved a correlation coefficient of 0.98, confirming its high accuracy in experimental data fitting. By effectively capturing oscillatory phenomena during both unloaded and loaded startup conditions, this work establishes the model as a reliable representation of belt conveyor dynamics, setting a new benchmark in the field.

With rapid development in information gathering technologies and access to large amounts of data, we always require methods for data analyzing and extracting useful information from large raw dataset and data mining is an important method for solving this problem. Clustering analysis as the most commonly used function of data mining, has attracted many researchers in computer science. Because of different applications, the problem of clustering the time series data has become highly popular and many algorithms have been proposed in this field. Recently Swarm Intelligence (SI) as a family of nature inspired algorithms has gained huge popularity in the field of pattern recognition and clustering. In this paper, a technique for clustering time series data using a particle swarm optimization (PSO) approach has been proposed, and Pearson Correlation Coefficient as one of the most commonly-used distance measures for time series is considered. The proposed technique is able to find (near) ...

This study brings up the torque ripple characterization with its reduction and provides a reducedorder modelling along with its simulation framework for a BLDC motor. This study integrates the electrical and mechanical dynamics equations with the generation of trapezoidal back-EMF based torque. Observation and analysis of torque ripple is done under varying transient and steady-state operation. Back-EMF-based current shaping technique is used to reduce torque oscillations and improve the torque smoothness. Voltage-speed mapping is implied for the regulation of speed rather than current control which was used conventionally. To verify performance of speed tracking, torque stability, current constraints and reduction in ripple is done by an automated validation framework. Stable system behavior with reference-dependent speed responses and acceptable current magnitudes are received as the simulation results of this study.

The distribution system planning and operating present significant challenges due to low voltage, high impedance, and large load density, which lead to substantial power losses and low voltage quality. To address this challenge, the paper proposes an optimal framework for the simultaneous determination of the placement and sizing of static VAr compensators (SVCs) in DSs. The proposed model is formulated as an optimization problem that minimizes the life cycle cost, while accounting for the varying lifespans and investment times of SVCs. The framework incorporates hourly load variation and employs full alternating current (AC) power flow analysis to improve the accuracy of results. Additionally, it considers the dependency of the reactive power injected by SVCs on the DSs and incorporates the discrete rated capacities of SVCs to ensure practical feasibility and enhance the accuracy of compensation power, effect of DSs. The proposed approach is validated using a modified 33-bus IEEE test system implemented in the general algebraic modeling system (GAMS). Numerical results from multiple case studies confirm the feasibility and high performance of the proposed model.

The power network faces several challenges as electricity usage rises, and the frequency of partial and total grid disruptions is of significant concern. This paper addresses the problem of voltage instability and high-power losses in transmission networks, which threaten the stability of the power grid. MATLAB R2023a/MATPOWER 5.0 is used to develop a model and analyse it using the Newton-Raphson load flow method. The analysis reveals a marginal voltage violation at Bus 13 (below 0.95 p.u.). To enhance stability and efficiency, the network was reconfigured using a hybrid whale algorithm and particle swarm optimisation (WAPSO) approach, incorporating new transmission lines (5-8 and 13-14) to improve connectivity and reduce congestion. The reconfiguration reduced active power losses by 29.5% (from 36.013 to 25.371 MW) and reactive power losses by 29.8% (from 301.30 to 211.59 MVAr). The system demonstrated first swing stability, with rotor angles remaining below π/2 (1.5669 rad maximum deviation) and fault clearance within the critical clearing time (0.2 s). Optimised exciter gains and a damping coefficient of 1.5 p.u. ensured effective oscillation suppression and stable generator voltages at 1.05 p.u. The hybrid WAPSO approach proved effective in optimising voltage and rotor angle stability, enabling the network to meet a 24.086 p.u. load demand while enhancing overall grid reliability.

En este artículo, se desarrolló un algoritmo, basado en la técnica de enjambre de partículas (Siglas en inglés -PSO) para optimizar los procesos de fundición a presión y por compresión, utilizando modelos matemáticos para describir el comportamiento de ambos procesos. En la fundición por compresión el modelo matemático describe un problema con múltiples objetivos y restricciones, y en la fundición a presión el modelo describe un problema de un solo objetivo con restricciones. El desarrollo del algoritmo PSO se realizó con el software FORTRAN 90, y los resultados se compararon con los obtenidos usando un algoritmo de optimización basado en el proceso de enseñanza-aprendizaje (Siglas en inglés -TLBO), demostrando buenas capacidades en la optimización de parámetros en fundición a presión y por compresión. Se observó que con el algoritmo PSO se consigue una solución óptima en todos los procesos y los tiempos computacionales fueron mínimos. Palabras clave: Optimización de parámetros, fundición por compresión, fundición a presión, algoritmo PSO.

The main goal of this research is to use Simulink to optimise quarter-car semi-active suspension systems employing variable spring configurations. Semi-active suspensions are advanced systems that adjust damping characteristics in real-time to achieve a balance between ride comfort and handling performance. The purpose of this study is to investigate the possibility of improving overall suspension performance by adding a second spring in parallel with the main suspension spring. A precise model of the quarter-car suspension system will be developed using Simulink. The vehicle mass, primary suspension spring, semi-active damper, and extra spring will be all included in the model. The semi-active damper will be controlled by a PID controller. The research includes simulating parallel and series configurations of the additional spring to analyze their effects on suspension behavior. Key aspects under investigation include how these configurations impact ride comfort, handling stability, and the distribution of forces within the suspension system. This research contributes to advancing the understanding of how variable spring configurations can be utilized to optimize semi-active suspension systems, offering insights that may benefit automotive industry in designing more effective and efficient suspension solutions for modern vehicles.

Fish bones in fillets can be serious problem both to the consumer and to the processing company. Filets with bones are likely to reduce the consumption especially in traditionally weak fish consumption areas. Fish bones can be dangerous leading to wounds in the human digestion tract. Fish, such as cod, have fine structured skeleton and the bone density is lower than that of man. Five cods where scanned with a normal hospital computer tomography (CT) scanner. Their skeletons were segmented out of the data set and a model reconstructed. The model is evaluated by visual inspection and compared to the cod to see what is missing from the skeleton. The density of the bones and otoliths is measured. The results show an almost complete skeleton with thin bones compared to the pixel size of image and with bones that are considerably lower in density than trabecular human bones.

This paper explores an enhanced method for regulating the speed of brushless DC (BLDC) motors using field-oriented control. Conventionally, a proportional-integral (PI) controller is employed to adjust output speed and current FOC method. While the PI controller is effective in many scenarios, it exhibits limitations including poor performance when the speed reference changes rapidly. To address these limitations, a fuzzy-PI control scheme is proposed in this study with the aim of improving the speed control performance of BLDC motors, especially under rapidly changing speed reference. The proposed two separate fuzzy logic controllers adaptively adjust the proportional and integral gains so that it combines the robustness of fuzzy logic with the steady-state error of PI control. Simulation and experimental results demonstrate that the fuzzy-PI control significantly outperforms the conventional PI controller in terms of BLDC stability, response time, and accuracy. The proposed approach ensures more reliable and efficient speed regulation for BLDC motors, making it a reliable solution for applications where speed reference fluctuate frequently.

In today's world, the rise in global warming is driving a shift towards electric mobility. The progress in battery technology and power electronic devices has facilitated the transition of vehicles from being powered by traditional internal combustion engines to electric motors. The types of motors utilized for propulsion include DC motors, three-phase induction motors, permanent magnet synchronous motors (PMSM), and brushless DC motors (BLDC). Among them, the BLDC motor, when paired with a suitable control algorithm, proves to be the most suitable option for electric vehicle applications. The existing control algorithms for BLD motors are quite complex. Therefore, this study presents the development of an innovative and simple digital control algorithm based on a combinational logic circuit to drive the BLDC motor under motoring and regenerative braking mode. The proposed control algorithm and its effectiveness are validated by simulating it using Xilinx & Proteus software and experimenting with the concept in hardware by utilizing a PIC microcontroller. The proposed control algorithm forms a cost-effective alternative for BLDC motor speed control.

Robust and effective control of brushless dc (BLDC) motors is paramount in modern-day motion control. The BLDC motor is known for its high speed, high torque, small size, low noise, and equally low maintenance requirements compared to the brushed DC motor. Nowadays, it can be found in the areas of robotics, aerospace, military, and industrial machines, among others. In this paper, two inverter topologies, the threephase six-switch driver circuit (TPSSDC) and the three-phase four-switch driver circuit (TPFSDC), are used to drive and control the speed of the BLDC motor. For the control technique, however, a fitting neural network from the deep learning (DL) toolbox is employed to train and improve the speed performance of the motor. Both TPSSDC and TPFSDC are simulated and tested in MATLAB Simulink, and the resultant output is analyzed graphically and analytically. Graphical observation shows that the TPSSDC control approach is superior in terms of reference tracking and has less ripple, and better rise and settling times when compared to the TPFSDC control approach, however, the TPFDC is considered for its low cost and simple circuitry. Numerically, the TPSSDC also outperforms at 1000 rpm with a 1.685 ms rise time and a 1.420 ms settling time compared to the TPFSDC, which rises at 6.526 ms and settles at 5.237 ms. At 3000 rpm motor speed, the TPSSDC is better, having a rise time of 5.815 ms and settling time of 4.048 ms, when compared to the TPFSDC, which rises at 11.277 ms and settles at 10.067 ms.

This research was conducted to optimize energy consumption forecasting in the commune of Boma, in the Democratic Republic of Congo, in the face of persistent imbalances between energy production and demand. The main objective of the study was to assess local energy needs in order to support the economic and social development of the region. To achieve this objective, a methodology integrating quantitative and qualitative techniques was adopted. Data were collected through surveys conducted among residential, semi-industrial, and tertiary consumers, as well as demographic information provided by the town hall. In parallel, machine learning techniques were employed to predict energy consumption, with the Particle Swarm Optimization (PSO) algorithm used to optimize forecasts. The forecasting model was accompanied by statistical analyses, including the Pearson correlation coefficient and the Student t-test, to validate the results. The analysis revealed a very high correlation between actual and predicted values, with a coefficient reaching 0.999, which demonstrates high model accuracy. However, biases were observed, including a tendency to overestimate energy consumption, highlighting the importance of reliable data collection to improve forecast accuracy. In conclusion, the PSO algorithm has proven to be an effective tool for energy demand management, although adjustments are necessary to optimize the results. The lessons learned highlight the need for a thorough understanding of consumption behaviors and regular data updates to adapt forecasts to future developments.

Electrohydraulic servo actuator mathematical model is one of the highly nonlinear hydraulic models. Electrohydraulic servo actuators used by aerospace, industrial and robotics applications for which accurate and fast performance are required in the presence of large loads or external disturbances. A servo actuator is made of a hydraulic cylinder or rotary actuator that is closely connected to an electrohydraulic servo actuator.This work focuses on modeling and simulation of electrohydraulic servo actuator that used for position control of the flight surface in a military aircraft with presence of external forces. PI-like Fuzzy Logic position intelligent Controller (FLC) is designed and simulated to control the actuator desired position during a specified time with minimum steady state error, settling time and oscillations in position response. This controller is implemented by using MATLAB Simulink and it has a settling time of 0.168 (sec). By comparison with reference [3], which ha...

Improving the dynamic performance of an automobile suspension system is considered as the main demand for comfortable and safe passenger travelling. From all previously proposed and implemented works, it is noticed that there are other factors that need to be considered to raising the car holding and stability in the road for improved passenger comfort when travelling. The minimization of car body displacement and oscillation time after exposure to road disturbances have been adopted in this work due to their contribution in raising the car holding and stability. The improvement in these features was maintained via a robust control methodology. The Fractional Order PID controller tuned by the Whales Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO) algorithm is suggested in this work as a robust controller to reduce the effect of these demerits. In this paper, an active quarter car suspension nonlinear system is designed for the presented goals using a robust contro...

The short form of Very Highly Advanced Artificial Intelligence is VHAAI. The short form of International Society for VHAAI is ISVHAAI. VHAAI field is used by ISVHAAI Artificial Intelligence Society to address various problems. GOD Devotee Human Swarm Optimization (GDHSO) is the novel algorithm designed in this Letter No. 13 of ISVHAAI AI Society Letters.

The Nigerian Power system network in recent times has witnessed various power system distortions and frequent voltage violations leading to voltage collapse arising from inadequate reactive power support. The paper deals with enhancement of voltage stability in 330 kV Nigeria transmission network using  Artificial Neural Network (ann) and Particle Swarm Optimization (PSO) techniques; UPFC FACTS tuned by ANN and determination of weak buses and its transmission  line location via PSO optimization algorithm were implemented. By addressing the voltage stability issues in the network, the study aimed to contribute to system reliability and efficiency. Power flow was carried out using PSAT in steady state without UPFC, then with UPFC, and subsequently with ANN and PSO. Without UPFC FACTS, the following buses had least voltages which is a violation. The buses are 3, 6, 7, 8, 11, 22, 30, 34, 40, 54 and 55. Their recorded voltages were (301.26, 300.97, 302.78, 305.46, 301.57, 300.357, 301.71, 300.46, 300.34, 301.62 and 301.1) kV with an average of 301.6 kV. With UPFC, the bus voltages improved to (323.4, 322.25, 322.73, 321.48, 323.43, 321.53, 322.66, 322.75, 321.15, 320.55, 321.29) with an average of 322.09 kV. With the introduction of ANN, the bus voltages improved to (326.5, 327.69, 328.33, 329.99, 329.27, 326.13, 326.45, 326.71, 325.47 and 327.99 kV with an average bus voltage of 327.5472 kV. seventy percent 70% of the data obtained was for the training of the ANN based on Levenberg Marquadt back propagation algorithm and fifteen percent 15% each was for testing and validation.  The average steady state voltage improvement with UPFC FACTS is 6.7% whereas with ANN it was 8.6%. In steady state, ANN performed better than UPFC FACTS. In dynamic state, at the generation stations of hydro and gas turbines UPFC performed better than ANN as the amplitude of ANN voltages experienced serious instability and fluctuations; whereas UPFC had a stable voltage of 7.5 kV. The implication is reduced insulation requirements and dimensions of synchronous alternators resulting to lower cost of alternator manufacturing.

A new core sampler was developed. Its sampler pipes are made of acrylic acid resin to be convenient to examine the stratification of a core. A fur of seal is pasted inside the sampler cutter in order to prevent the loss of sample when the sampler is pulled up. The sampler joint was also deviced to be suitable for jointing the sampler pipes. The sampler was used for the measurement of water equibalence of snow covers and for stratigraphic observations within them. All devices were successful. It was observed that the amount of contraction of a core depended on snow form and sampling procedure, and the value of the ratio of the snow depth to the core length varied from 1. 04 to 1. 35. In order to reproduce the stratification within a snow cover from that in a core, it is suggested that the height of an interface within the snow cover is, as an approximation, estimated from multiplying that of the corresponding interface by the value of the ratio of the snow depth to the core length. It is shown that the error of this estimated height is less than 8. 4% of the snow depth. A method for the improvement on this error is suggested. The new devices for the sampler are applicable to core samplers used for other deposits as well as snow cover.

In engineering works, calculation of the peak zone of the flood is very important. Therefore, in the present study, a method was presented to increase the accuracy of the flood routing of the peak zone of the inflow hydrograph. The recorded data in the Ahwaz and Farsiat hydrometric stations were used, both of which are related to the Karun river, Iran. In contrast to previous studies, in addition to calculating the coefficients of linear Muskingum method (X, K), the time interval (Δt) parameter was also optimized in the present study using the PSO algorithm. The results showed that if only the X and K coefficients were calculated, the mean relative error (MRE) of the peak zone for the first, second and third floods were equal to 8.34, 2.24, and 1.99 %, respectively. However, by using the optimized Δt value, the corresponding error decreased to 5.14, 0.44, and 1.08 %.

The main aim of this paper is the utilization of discrete chaotic Lozi map based chaos number generator to enhance the performance of PSO algorithm. This paper presents the results of research, in whichchaos enhanced PSO algorithm is used to design an optimal PID controller for DC motor system. Obtained results are compared with other non-heuristic and heuristic methods.

One of the best flexible AC transmission system (FACTS) is unified power flow controller (UPFC). As it gets more benefit from both real and reactive power transfer, it is used in power system for controlling the transmitted power. The UPFC controls the power on the transmission side of the power system. When the real as well as reactive power is set the UPFC tries to follow the command by using the proportional and integral (PI) controller. But in some power systems the PI controllers cannot produce the proper power due to the power oscillations. These oscillations are created due to PI controller properties. In this paper the PI controller is replaced with the particle swarm optimization tuned PI controller (PSO-PI). It minimizes the power oscillations by using the objective function. The MATLAB 2017b is used to demonstrate the power transfer curves and the voltages. The IEEE 9 bus system is being used as a reference system.

The loss minimization is a major role in Power System (PS) research. Transmission line losses in a PS can be reduced by Var compensation. After the establishment of power markets with transmission open access, the significance and use of Flexible AC Transmission Systems (FACTS) devices for manipulating line power flows to relieve congestion and maximize the overall grid operation have been increased. This paper presents a method to provide simultaneous or individual controls of basic system parameters like transmission voltage, impedance and phase angle, which are controlling by using Unified Power Flow Controller (UPFC). The Particle Swarm Optimization (PSO) method is used to compute the power flow in optimum value. The performance of this technique is tested using IEEE - 14 bus system through the MatLab/Simulink simulation software package. The simulation results of test power system show that the location of the UPFC has been able to enhance the voltage level of the test power sy...

As I complete my research work, I realize and recognize numerous hands that have helped me in many ways, and I thank them all with my whole heart. I express my sincere gratitude to my supervising guide Dr. Mariamma Chacko, for the zeal with which she guided me in carrying out my Ph. D study and research work. It has been a great learning experience working with her. I am deeply indebted to her for her valuable guidance, patience, constant encouragement and suggestions throughout the course of my work. I would like to thank Dr. Sumam Mary Idicula, Computer Science Department who is a member of Doctoral Committee, for her valuable guidance and insightful comments for the improvement of my work. I am also indebted to Dr. James Kurian & Dr. Nandakumar, for supporting me all throughout the process. I would like to thank all the department research committee members for their comments, encouragement and also for their questions which widened my research from various perspectives. I remember my father with deep love for his blessings bestowed upon me. I am also thankful to my mother, my husband Ranjan, son Vaishnav and daughter Reshma for their support and prayers in my hard times without which I would not be able to complete this thesis. I also extend my gratitude to my fellow research scholars and my friends who always stood by my side during my difficult times. I thank God Almighty for the uncountable blessings bestowed upon me all through my life and especially during the period of my thesis work. With a heart full of gratitude, I submit this thesis. Once again I thank all who walked with me to make this venture a grand success.

As I complete my research work, I realize and recognize numerous hands that have helped me in many ways, and I thank them all with my whole heart. I express my sincere gratitude to my supervising guide Dr. Mariamma Chacko, for the zeal with which she guided me in carrying out my Ph. D study and research work. It has been a great learning experience working with her. I am deeply indebted to her for her valuable guidance, patience, constant encouragement and suggestions throughout the course of my work. I would like to thank Dr. Sumam Mary Idicula, Computer Science Department who is a member of Doctoral Committee, for her valuable guidance and insightful comments for the improvement of my work. I am also indebted to Dr. James Kurian & Dr. Nandakumar, for supporting me all throughout the process. I would like to thank all the department research committee members for their comments, encouragement and also for their questions which widened my research from various perspectives. I remember my father with deep love for his blessings bestowed upon me. I am also thankful to my mother, my husband Ranjan, son Vaishnav and daughter Reshma for their support and prayers in my hard times without which I would not be able to complete this thesis. I also extend my gratitude to my fellow research scholars and my friends who always stood by my side during my difficult times. I thank God Almighty for the uncountable blessings bestowed upon me all through my life and especially during the period of my thesis work. With a heart full of gratitude, I submit this thesis. Once again I thank all who walked with me to make this venture a grand success.

Permanent magnet brushless DC motors (PMBLDC) find broad applications in industries due to their huge power density, efficiency, low maintenance, low cost, quiet operation, compact form and ease of control. The motor needs suitable speed controllers to conduct the required level of interpretation. As with PI controller, PID controller, fuzzy logic, genetic algorithms, neural networks, PWM control, and sensorless control, there are several methods for managing the BLDC motor.  Generally, speed control is provided by a proportional-integral (PI) controller if permanent magnet motors are involved. Although standard PI controllers are extensively used in industry owing to their simple control structure and execution, these controllers have a few control complexities such as nonlinearity, load disruption, and parametric variations. Besides, PI controllers need more precise linear mathematical models. This statement reflects the use of Classic Controller and Genetic Algorithm Based PI, PI...

This paper will propose a novel technique for optimizing hydroelectric (hydropower plants) on a small scale based on load frequency control that uses the self-tuning fuzzy proportional derivative method. Due to the fact that frequency is not controlled by any dump load or something else, this power plant is under dynamic frequency variations that will use proportional-derivative controllers, which are optimized by fuzzy rules and then with deep learning techniques. The main purpose of this work is to maintain frequency in small hydropower plants at a nominal value. So, the proposed controller means Fuzzy Type-II-Proportional-Derivative will be used for load frequency control in a small hydropower system. The proposed schema can be used in different designations for both diesel generators and mini-hydropower systems at low stream flow. It is also possible to use a diesel generator in the hydropower system, which can be turned off when consumer demand is higher than electricity generation. The simulation will be done in MATLAB/Simulink to represent and evaluate the performance of this control schema under dynamic frequency variations. Spiking Neural Networks are used as the main deep learning techniques for optimizing this load frequency control, which turns into a Deep Spiking Neural Network. The obtained results indicated that the proposed schema has robust and high-performance frequency control in comparison to other methods.

This paper examines an IEEE standard test system and uses MATLAB to test it using the Newton-Raphson method. Each bus's voltage magnitudes are analyzed, and FACTS like SVC and TCSC are added to the appropriate weak bus. The L-Index approach can be used to determine the best location for FACTS. When the L-index value approaches unity, it indicates that instability has been reached. The stability of the system is enhanced under fault and steady state situations from this instability point. By altering the Load Reactive Power at a certain bus, the system becomes disturbed.

This paper discusses the effectiveness of two FACTS devices TCSC and UPFC in providing solutions to transmission line congestion problems. One is a series device, (TCSC) and another is the combination of series and shunt device, (UPFC). These FACTS devices were applied and tested on the South East Nigeria 11-bus system; using Power System Analysis Toolbox (PSAT) platform to compare their effectiveness in managing congestion.