Frequency Deviation

Load Frequency Control (LFC) and Automatic Voltage Regulation (AVR) are critical for maintaining stability and reliable operation in interconnected power systems. Conventional control methods, such as classical PID controllers, are widely used due to their simple structure and ease of implementation; however, their performance degrades under varying load conditions and system uncertainties. To improve system response, fuzzy PID controllers have been introduced, where membership functions are used to map input variables such as frequency deviation and its rate of change into fuzzy sets, enabling rule-based linguistic control. Although fuzzy controllers enhance transient performance, their effectiveness depends heavily on the proper design of membership functions and rule base. Further improvements have been achieved using optimization-based techniques such as NLTA, Modified Particle Swarm Optimization (MPSO), and Arithmetic Optimization Algorithm (AOA), which help in reducing overshoot and settling time. However, these methods lack real-time adaptability and struggle to handle highly nonlinear and dynamic operating conditions. To overcome these limitations, this paper proposes an Artificial Neural Network (ANN)-based controller for the LFC loop to provide adaptive and intelligent frequency regulation. The ANN effectively captures nonlinear relationships between system inputs and outputs, using frequency deviation and rate of change of frequency as inputs to generate an adaptive control signal. The AVR loop operates in coordination with the ANN-based LFC to ensure simultaneous voltage regulation. Simulation results demonstrate that the proposed ANN controller significantly improves system performance by enhancing dynamic response, reducing settling time, and maintaining stability under varying operating conditions and uncertainties. The proposed approach provides a robust and adaptive solution for modern LFC-AVR control.

Frequency stabilization in interconnected power systems has become increasingly challenging due to the integration of hybrid generation sources and varying load conditions. Conventional Load Frequency Control (LFC) methods, such as ANN-based coordinated control, utilize energy storage systems and thermal power units to enhance system performance. These approaches employ techniques like Wavelet Packet Decomposition for control signal generation and backpropagation neural networks for disturbance identification. Although effective, such methods suffer from high computational complexity and dependency on training data, limiting their real-time applicability. To address these challenges, this paper proposes a Teaching-Learning-Based Optimization (TLBO) tuned Proportional-Integral-Derivative (PID) controller for Automatic Generation Control (AGC). The proposed controller optimally adjusts its parameters using the TLBO algorithm, which requires fewer tuning parameters and offers faster convergence compared to other metaheuristic techniques. The optimization objective focuses on minimizing frequency deviations and tie-line power fluctuations across interconnected areas. The proposed TLBO-based PID controller is implemented in a multi-area power system incorporating diverse generation sources, including thermal and renewable units. Simulation results demonstrate that the proposed method significantly improves frequency stability, reduces settling time, and enhances overall system performance under varying operating conditions. Hence, the TLBO-optimized PID controller provides a robust and efficient solution for modern power system frequency regulation.

One of the concerns in power system preventive control and security assessment is to find the point where the voltage and frequency collapse and when the system forces a severe disturbance. Identifying the weakest bus in a power system is an essential aspect of planning, optimising and post-event analysing procedures. This paper proposes an approach to identify the weakest bus from the frequency security viewpoint. The transient frequency deviation index for the individual buses is used as the weakest bus identification as well as a frequency security indicator. This approach will help to determine the bus with the worst deviation, which helps to analyse the system disturbance, takes proper control action to prevent frequency failure, and most importantly, observes consumer frequency. The approach is applied to the WSCC 9 bus test system to show the feasibility of the method.

The frequency is one of the instruments for measuring the health status of the power system, due to its ability to anticipate any imbalance between generations and loads. If the generated power is adequate for the system load and losses, then, the system will be in a steady state, otherwise frequency deviates from nominal value due to the mismatch between the generation and load. If the frequency continues to deviate from nominal value, the system may collapse. The assessment of frequency stability level becomes an essential aspect of power system operation and also for projecting the ability of the power system to maintain nominal frequency when subjected to any disturbance. In this paper, a method is proposed to evaluate frequency security of multi-machine power system using transient frequency deviation index (TFDI) which is based on Center of Inertia (COI) referred frequency. The proposed method has been tested on the New England 39-bus test system. Results show that the proposed method takes the advantage of TFDI in accumulating the effect of frequency trajectory deviations. These frequency trajectories may be obtained from the time domain simulation or Wide Area Measurement System (WAMS). The results also show the advantages of COI referred frequency in representing the equivalent frequency of the system. The method would provide a reliable and efficient base for load shedding relays adjustment and operation control.

Among the significant renewable resources, wind power has acquired the spotlight, due to the quick development in wind energy conversion technology. However, the penetration level of wind power cannot be increased randomly for maximum power generation primarily because the wind farms cannot always provide a precise amount of power similar to the conventional synchronous generators. Since the frequency is the most affected by the penetration of wind power due to the variable nature and the less inertia of these turbines, it is necessary to estimate the maximum level of wind energy that can maintain a nominal frequency and does not affect the frequency stability of the power system. In this paper, new procedures to determine the maximum wind penetration level using transient frequency deviation index (TFDI) and based on the center of inertia frequency are presented. The results of procedures are established through the dynamic simulation of WSCC 9-bus test system. By using this method, the critical wind level can be assessed while the system preserves acceptable operation frequency.

In an Interconnected power system, as a power demand varies randomly, both area frequency and tie-line power interchange also vary. Load frequency control (LFC) is of importance in electric power system operation to damp frequency and voltage oscillations originated from load variations or sudden changes in load demands. The objectives of load frequency control(LFC) is to minimize the transient deviations in these variables (area frequency and tie-line power interchange) and to ensure their steady state errors to be zeros. The control error signal in LFC is called area control error (ACE) which is a linear combination of net tie-line power error and frequency error. This paper presents the analysis of the load frequency control (LFC) of a realistic two area interconnected power system having diverse sources of power generation. A Thyristor Controlled Phase Shifter (TCPS) is used in series with AC tie line for improving the dynamic performance of the LFC system. The power system simulation is done using MATLAB Simulink and control problem is solved using MATLAB programming. An optimal integral controller with and without TCPS are obtained following a step load perturbation in either of the areas using Particle Swarm Optimization (PSO) algorithm with a strong ability to find the most optimistic results. Simulation results show that due to the presence of TCPS, the dynamic performance in terms of settling time and overshoot is greatly improved. The system with TCPS is capable of suppressing the area frequency and tie line power deviations more effectively under the occurrence of area load perturbations.

An islanded Microgrid (MG) might experience extreme frequency deviations which necessitates a reliable frequency control scheme to ensure its frequency stability. Various techniques have been proposed including PI control and more advanced techniques like fuzzy control. The PI controls cannot establish a fast frequency restoration due to their poor performance and slow response-time of DGs. Advanced methods have been able to slightly improve the DGs response-time and hence decrease the total frequency deviation. Here, a novel frequency control scheme is proposed that despite its simplicity than previous schemes is able to further decrease the total frequency deviation. The proposed is tested on the CIGRE low voltage MG. The simulation results show that using the proposed frequency control scheme, the value of the total absolute frequency deviation is decreased by 79% and 68 % than using the GA-optimized and fuzzy frequency schemes, respectively.

Distance relays are protective devices which main goal is the protection of transmission lines. However, the presence of harmonics and the exponentially decaying DC offset in the system voltage and current signals negatively affects the relay performance. In this paper, an adaptive phasor estimation method based on Artificial Neural Networks is presented to reduce these components' effects, focusing on impedance estimation for distance relaying. The method uses the multilayer perceptron architecture to estimate the current and voltage signals, and then proceeds to calculate the complex apparent impedance during a continually online training process. This online training allows for adaptability regarding the system frequency, providing tolerance against its deviation. Graphical results of the test cases are presented, comparing the functionality and performance of the proposed algorithm with a Fourier-based method.

Majority of the contemporary hierarchical control strategies for microgrids are either centralized or distributed, relying on leader-follower (or master-slave) consensus for secondary frequency and/or voltage regulation. Thus, in either case, these strategies are susceptible to single-point-failure (SPF). This potential research gap motivated the authors to propose a distributed three-layered hierarchical control strategy applicable to droop-controlled islanded AC microgrids. The proposed strategy can simultaneously ensure (i) frequency and voltage regulation of DGs within the prescribed frequency and voltage deviation limits as per IEC 60034-1 standard (i.e., ±2% and ±5%, respectively) without relying on leader-follower consensus at the secondary level, (ii) distributed economic dispatch of active power with minimum error, and (iii) distributed reactive power dispatch with plausible error. The proposed technique is fully distributed and shares the computation and communication burden among the neighboring nodes using a sparse communication network, thus, it is insusceptible to SPF. The feasibility of the proposed technique is guaranteed through various time-domain based numerical simulations executed in Matlab/Simulink under different loading conditions and microgrid expansion.

In power systems, frequency constitutes a parameter indicating the equilibrium between power demanded by load and energy produced by generation systems. This chapter studies the effects of varying different system parameters on the overall performance of the traditional frequency regulation system when including contributions of renewable energy sources. A model for the inclusion of variable speed wind turbines in the frequency control loops is analyzed, and parametric sensitivity functions are established using linearized models and transfer function representations for the system components. Through both theoretical analysis and performance simulations, the impact of an inaccurate representation of system inertia in frequency performance is established. Stability analysis for inertia sensitivity of frequency regulation involving wind generation is also provided. Results indicate more robustness to parameter variations for systems including wind turbine participation. However, the frequency deviation rate increases when the uncertainty in system parameters grows. This behavior might lead to instability scenarios under frequency disturbances for the power system.

Correct sizing of grid-connected inverters is a fundamental requirement for the safe, compliant, and yield-optimised operation of photovoltaic (PV) power systems. The DC/AC ratio-defined as the ratio of the PV array's peak DC power to the inverter's rated AC output-is the primary design variable in inverter selection, and its optimisation requires simultaneous verification of five interdependent compliance criteria: DC/AC ratio within preferred limits, total AC capacity adequacy, AC coverage sufficiency, inverter unit count compliance, and MPPT voltage window compatibility. This article presents the mathematical framework underpinning IEC-compliant inverter sizing, discusses systematic design errors encountered in commercial PV practice, and introduces a freely accessible computational tool that applies this methodology against real manufacturer inverter data sourced from a validated component database.

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Reliable evaluation of system strength is crucial for optimal design and enhanced control of the International Thermonuclear Experimental Reactor (ITER) Multi-Infeed Direct Current (MIDC) system. As electromagnetic induction-induced disturbances from the system's coupled magnet coil configurations on the performance of critical subsystems render traditional evaluation methods such as Short Circuit Ratio (SCR) inaccurate, a robust evaluation of system strength is essential. This paper proposes the Autonomous Short Circuit Ratio (AutsSCR) framework for authentic assessment of the ITER MIDC system strength. By integrating Active Disturbance Rejection Control (ADRC) mechanisms to enable real-time total disturbance compensation, the framework restores the expected strength metrics, specifically the Maximum Power Curve (MPC) and Maximum Available Power (MAP), while enhancing both the efficacy and precision of the assessment. A 4.04% enhancement in evaluation efficiency was achieved under the AutsSCR framework. Furthermore, the Apparent Increase in Autonomous Short Circuit Ratio (AIASCR) index is developed to quantify the Static Var Compensator (SVC)'s contribution to system stability, with its effects measurable in terms of system strength values, specifically reflected in changes to MAP and Temporary Overvoltage (TOV). The optimization of SVC control significantly enhances voltage stability, yielding efficiency improvements of 68.7% (QFC) and 87.3% (QLC) compared to QMV. Validation through analytical models and equivalent simulations on the MATLAB platform demonstrates the efficacy of the proposed measures. This work not only improves evaluation accuracy for strongly coupled ITER MIDC systems but also establishes a transferable assessment paradigm for related networks through anti-interference mechanisms that mitigate subsystem interactions, thereby advancing the portability of Single-Infeed Direct Current (SIDC) evaluation standards.

A modern electric power plant is typically considered to be a large-scale system. Due to continual and random occurrence of load changes, maintenance of network frequency (or load frequency control LFC) at its nominal value is one of the most crucial control problems in order to ensure the stability and reliability of such an electric power grid. This study investigates a new efficient integration of fuzzy logic controllers based on PD principle and superconducting magnetic energy storage (SMES) devices in an effort to protect the system frequency from the load variations. It is well known that the PD-based fuzzy logic controllers, when applied to an LFC strategy, are capable of damping quickly the oscillations of both the system frequency and tie-line power deviations. In addition, the load disturbances can be compensated if the network is applying the SMES devices. Therefore, the integration between them might become an efficiently feasible solution for the LFC issue. The superiority of the proposed control methodology over conventional regulators is verified through a number of numerical simulations which will be implemented in this study for a five-area electric power grid model.

Micro hydro power plants (MHPP) is one of the renewable energy that can be utilized as a distributed generation with controllable power output. One common issue in MHPP systems is the non-constant rotation of the generator caused by load fluctuations. This instability leads to variable frequencies, which can potentially harm electrical equipment. To address this problem, the volume of water entering through the governor can be adjusted to synchronize the turbine and generator rotation with the load. This approach helps dampen frequency oscillations and ensures that the system operates within desired limits. Therefore, there is a need for technology that can enhance the performance of micro hydro power plant units, specifically load frequency control (LFC). This research proposes the application of the grey wolf optimizer (GWO) algorithm to optimize the PID controller parameters for MHPP LFC. MHPP has been modeled in both isolated and grid-connected modes using Simulink MATLAB R2020a. The best cost function value for an isolated mode system was obtained with ISEim, yielding a value of 0.067653, while for a grid-connected mode system, it was achieved with ISEgm, with a value of 0.015861. The results of the frequency deviation response performance of the LFC using GWO indicate that the fastest settling time was achieved with the cost function ITAEim in isolated mode, and with IAEgm in grid-connected mode. The cost function that produces the smallest peak overshoot and peak undershoot parameter values varied depending on changes in the system load.

In recent decades, renewable energy sources, such as wind power, have extraordinarily increased their participation in the energy mix throughout the world. This progression has played an important role in lowering the usage of fossil fuels. In addition, it has reduced environmental hazards and increased the emergence of hybrid power systems, mainly in remote areas. In some of these areas, diesel power plants were the only previous source of energy. Irrespective of the benefits, hybrid power systems might face problems such as frequency deviations. To contribute to reducing these problems, this paper presents a methodology to tune diesel engine governors using the Student Psychology-Based Algorithm. This proposed methodology enhances some metrics of controller performance, such as the integral square error, integral absolute error, and number of sign changes in the frequency derivative. This approach has been tested against different perturbations (step, ramp and random). To validate the effectiveness of the proposed approach, it has been simulated in relation to the San Cristobal Island (Ecuador) hybrid wind-diesel power system. The simulation results show that the governor tuned with the proposed approach provides a better system response.

In modern microgrid systems, maintaining frequency stability in the presence of renewable energy sources remains a critical challenge. This paper presents a resilient frequency control strategy that incorporates virtual inertia to mitigate instability issues arising from the fluctuations of renewable generation. The proposed method is based on the Robust Polynomial-Based Control Design (RPCD), which is tailored for microgrid applications to handle inherent system uncertainties. In addition, the H∞ control method is utilized as a benchmark to evaluate system performance under disturbances. The study considers distributed generation from solar and wind sources and analyzes the system's dynamic behavior in both grid-connected and islanded operating modes. In the islanded scenario-triggered by events such as voltage drops or frequency mismatchesthe performance of the developed RPCD-based scheme in preserving system frequency is thoroughly evaluated. Furthermore, to investigate potential enhancements in local control performance, a version of the system with optimized PID parameters using the Shuffled Frog Leaping Algorithm (SFLA) is also examined. The results confirm that the proposed controller, with and without optimization, successfully ensures system stability under diverse and challenging operational conditions.

In an electric power grid that has a high penetration level of wind, the power fluctuation of a large-scale wind power plant (WPP) caused by varying wind speeds deteriorates the system frequency regulation. This paper proposes a power-smoothing scheme of a doubly-fed induction generator (DFIG) that significantly mitigates the system frequency fluctuation while preventing over-deceleration of the rotor speed. The proposed scheme employs an additional control loop relying on the system frequency deviation that operates in combination with the maximum power point tracking control loop. To improve the power-smoothing capability while preventing over-deceleration of the rotor speed, the gain of the additional loop is modified with the rotor speed and frequency deviation. The gain is set to be high if the rotor speed and/or frequency deviation is large. The simulation results based on the IEEE 14-bus system clearly demonstrate that the proposed scheme significantly lessens the output power fluctuation of a WPP under various scenarios by modifying the gain with the rotor speed and frequency deviation, and thereby it can regulate the frequency deviation within a narrow range.

Frequency stability control in multiple asynchronous grids is a challenging and complex issue. An adaptive droop control strategy to improve the frequency regulation of asynchronous AC areas connected by a multi-terminal DC grid is proposed here. The droop coefficients are adjusted to share the active power adaptively among multiple asynchronous AC grids based on the characteristics of frequency deviation and rate of change of frequency. This results in a cogent allocation of imbalance power in multiple asynchronous grids from the frequency variation perspective. The performance of the proposed scheme is evaluated in a modified multi-machine power system using DIgSILENT Power Factory. Simulation results under significant frequency disturbances caused by credible contingencies are presented to demonstrate the effectiveness of the proposed approach. It is found that the proposed adaptive control ensures an excellent and robust frequency response under different operative conditions.

Microgrids have a low inertia constant due to the high penetration of renewable energy sources and the limited penetration of conventional generation with rotating mass. This makes microgrids more susceptible to frequency stability challenges. Virtual inertia control (VIC) is one of the most effective approaches to improving microgrid frequency stability. Therefore, this study proposes a new model to precisely mimic inertia power based on an energy storage system (ESS) that supports low-inertia power systems. The developed VIC model considers the effect of both the DC-DC converter and the DC-AC inverter on the power of the ESS used. This allows for more precise and accurate modeling of the VIC compared to conventional models. Moreover, this study proposes a fractional-order derivative control for the proposed VIC model to provide greater flexibility in dealing with different perturbations that occur in the system. Furthermore, the effectiveness of the proposed fractional-order VIC (FOVIC) is verified through an islanded microgrid that includes heterogeneous sources: a small thermal power plant, wind and solar power plants, and ESSs. The simulation results performed using MATLAB software indicate that the proposed VIC scheme provides fast stabilization times and slight deviations in system frequency compared to the conventional VIC schemes. The proposed VIC outperforms the conventional load frequency control by about 80% and the conventional VIC model by about 45% in tackling load/RESs fluctuations and system uncertainty. Additionally, the studied microgrid with the proposed FOVIC scheme is noticeably more stable and responds faster than that designed with integer-order derivative control. Thus, the proposed FOVIC scheme gives better performance for frequency stability of low-inertia power systems compared to conventional VIC schemes used in the literature.

A novel active islanding detection method using d-axis disturbance signal injection with intelligent control is proposed in this study. The proposed active islanding detection method is based on injecting a disturbance signal into the system through the d-axis current which leads to a frequency deviation at the terminal of the RLC load when the grid is disconnected. The feasibility of the proposed method is evaluated under the UL1741 anti-islanding test configuration. The proposed d-axis disturbance signal injection method is intended to achieve a reliable detection with quasi zero non-detection zone (NDZ), minimum effects on power quality and easy implementation without additional sensing devices or equipments. Moreover, to further improve the performance of islanding detection method, a wavelet fuzzy neural network (WFNN) intelligent controller is proposed to replace the proportional-integral (PI) controller used in traditional injection method for islanding detection. Furthermore, the network structure and the on-line learning algorithm of the WFNN are introduced in detail. Finally, the feasibility and effectiveness of the proposed d-axis disturbance signal injection method is verified with experimental results.

A detailed theoretical analysis is given for the impact of finite frequency deviation on the sensitivity of dual-filter heterodyne Frequency Shift Keying (FSK) lightwave systems. Our analysis provides closed-form signal-to-noise ratio (SNR) results for estimating the bit-error-ratio (BER) performance of the system. These closed-form results provide an insight into the impact of finite frequency deviation 2Afd, laser linewidth Am, bit rate R 6 , and IF filter bandwidths on the system performance. It is shown that there is a well-defined relationship between the choice of frequency deviation and the tolerable amount of laser phase noise. When there is no phase noise, a frequency deviation of 2Afd = 0.72R6 is sufficient for 1 dB sensitivity penalty with respect to infinite frequency deviation case; whereas for a linewidth of Am = 0.50R6 the required frequency deviation increases to 2Af 4 = 3.42P6 for the same sensitivity penalty. The sensitivity degradation can be very severe for a fixed linewidth as the frequency deviation gets smaller: for a linewidth of 20% the sensitivity penalty is only 0.54 dB when the frequency deviation is infinite whereas it is 3.48 dB when the frequency deviation is 2Afd = R. We also quantify the impact of finite frequency deviation on optimum IF filter bandwidths. For a fixed linewidth, the optimum IF filter bandwidth decreases as frequency deviation becomes smaller: for Am = 0.5R the optimum IF filter bandwidth reduces from 714 to 3R1 when 2Afd reduces from very large values to 314.

In this paper, we present a simple, distributed algorithm for frequency control and optimal economic dispatch of power generators. In this algorithm each generator independently adjusts its power-frequency set-points of generators to correct for generation and load fluctuations using only the aggregate power imbalance in the network, which can be observed by each generator through local measurements of the frequency deviation on the grid. In the absence of power losses, we prove that the distributed algorithm eventually achieves optimality i.e. minimum cost power allocations, under mild assumptions (strict convexity and positivity of cost functions); we also present numerical results from simulations to compare its performance with traditional (centralized) dispatch algorithms. Furthermore, we show that the performance of the algorithm is robust in the sense that even with power losses it corrects for frequency deviations, and for low levels of losses, it still achieves near-optimal allocations; we present an approximate analysis to quantify the resulting suboptimality.

As the world increasingly focuses on renewable energy sources, grid-connected PV systems will continue to play a critical role in meeting our energy needs. This integration has brought many benefits, but has also created various problems related to power quality and stability at the connection points. Various techniques are used to improve the power quality, such as passive filters, tuned passive harmonic filters, and active filters. This paper presents the use of a series active filter on the DC side of grid-connected PV systems to improve their power quality, stability, and dynamic performance. The proposed filter consists of an inductor, two capacitors, and four transistor-diode pairs, the operation of which is controlled by a sinusoidal pulse width modulation (SPWM) scheme. The MATLAB/Simulink is used to build and validate a comprehensive mathematical model of the studied system. The transfer function of the system is identified and its transient response is studied under various test input signals. A Bode plot is also generated to assess the impact of the integration of this component on the stability of the system. The results showed that incorporating the filter resulted in a significant reduction in the total harmonic distortion factor (Thd%) for both the voltage and current waves at the inverter output. Moreover, it improved the transient response characteristics of the system by significantly reducing the maximum overshoot value of the system response with respect to the test input signals. The results indicate that the stability of the system is improved after the filter addition, as evidenced by the increased gain margin and phase margin in the system Bode plot.

In this paper, a method for measuring the power frequency deviations has been presented. This technique is based on concept of counting the known train of pulses for the period corresponding to power frequency deviation. It has been simulated on SIMULINK tool of MATLAB and has been duly implemented. The technique is insensitive to harmonic noise due to its ability to filter out all the higher order components. With modest averaging, the approach is also relatively insensitive to offsets and random noise. The simulation results demonstrate a linear relationship between frequency deviation and counted pulses.

Individual microgrids can improve the reliability of power systems during extreme events, and networked microgrids can further improve efficiency through resource sharing and increase the resilience of critical end-use loads. However, networked microgrid operations can be subject to large transients due to switching and end-use loads, which can cause dynamic instability and lead to system collapse. These transients are especially prevalent in microgrids with high penetrations of grid-following inverter-connected renewable energy resources, which do not provide the system inertia or fast frequency response needed to mitigate the transients. One potential mitigation is to engage the existing generator controls to reduce system voltage in response to a frequency deviation, thereby reducing load and improving primary frequency response. This study investigates the use of a reinforcement-learning-based controller trained over several switching transient scenarios to modify generator controls during large frequency deviations. Compared to previously used proportional-integral controllers, the proposed controller can improve primary frequency response while adapting to changes in system topologies and conditions.

The grid-tied inverter synchronizes with the network on the basis of the instantaneous voltage phase angle. This angle is computed by the so-called synchronization algorithms. During grid disturbances, it is estimated with a certain accuracy, which varies for different disturbances and depends on the choice of algorithm. The tests presented here determine how to make an optimal selection of the synchronization algorithm. The research methods used are modeling, simulation and analysis of the results obtained. One of the most important outcomes is the determination of the root-mean-square sync error and its dynamics denotation. The research conclusions should be of particular interest to designers of distributed energy systems with a large number of inverter energy sources.

This paper presents a load frequency control in four area power systems using fuzzy gain scheduling of PI controller is realized. The system simulation is realized by using Matlab/Simulink software. System dynamic performance is observed for conventional PI, fuzzy PI and fuzzy logic controllers.

Recent years have witnessed considerable advances in designing and operating of energy grids, while the concepts related to the operation of Smart Grids, Vehicle-to-Grid, responsive demand, among others, are gaining research interest worldwide. From an educational viewpoint, the use of a complementary laboratory approach is expected to contribute to the effective communication of these concepts to the students of engineering courses at university level. To this end, this work presents the development of a virtual lab for educational purposes, simulating the operation of a Virtual Power Plant in a Smart Grid context. The system under study involves renewable energy generation, controllable loads and local electricity storage options from electric vehicles, while allowing the students to experiment with the system parameters and perform real-time monitoring and control of the system operation.

The oscillation of frequency can cause the generator to run out of sync in a power system. Therefore, load frequency control (LFC) is needed to reduce frequency oscillation and prevent out of sync operation. The LFC regulates the governor to balance the turbine speed with changes in the load of existing. In this paper, a proportional integral differential (PID) controller and a battery energy storage system (BESS) are added to an LFC to improve the frequency stability of a power system. The parameters of the PID controller and BESS are optimized using the Bat algorithm (BA) to attain a good coordination. The frequency performance analysis is done by introducing disturbance in the form of changes in load power. The simulation results show that the frequency deviation of the system with the PID controller and BESS, has a faster settling time and smaller overshoot value. The system performs better than those with only the PID controller or the BESS. In conclusion, the BA algorithm can be used to find optimal parameter values of the PID controller and BESS for a synchronized coordination of an LFC.

The CapEx of offshore floating wind turbine generation (WTG) and battery energy storage system (BESS) have declined over the years which increases the cost feasibility of replacing gas turbine power generation in offshore oil and gas (O&G) platforms. This paper presents a study of an integrated system consisting of an offshore floating wind turbine generation (WTG) and O&G production platforms with battery energy storage system (BESS) onboard to meet the load demand. It is shown in cost analysis that the integrated system consisting of BESS can lower overall cost in CapEx and OpEx, as compared with a typical system fueled by gas turbine generation. Moreover, transient stability in simulation has shown that the proposed system 2 has a significant reduction in both voltage and frequency transient deviations, with transient recovery time that could meet the IEC standards 61892-1 for O&G platforms.

Load frequency is an important issue in power system operation and control. In this paper, load frequency control for suppression frequency deviation in an interconnected power system with nonlinearities using SMC (sliding mode control) is studied. The governor dead band and GRC (generation rate constraint) is considered in this article. Digit simulations for both two areas and three areas power system with non-reheat turbines are provided to validate the effectiveness of the proposed scheme. The results show that, the robustness of the control method under parameters variation and different load disturbances with the SMC technique.

Optical labeling via frequency-shift keying (FSK) modulation capability of a grating-assisted codirectional coupler with sampled grating reflector laser is investigated for the first time. FSK modulation can be best implemented with only the phase section current and optical frequency deviation of 20 GHz is achieved. The measured frequency modulation response indicates the limitation of the maximum modulation rate is sufficient for Internet protocol packet optical labeling.

Optical labeling via frequency-shift keying (FSK) modulation capability of a grating-assisted codirectional coupler with sampled grating reflector laser is investigated for the first time. FSK modulation can be best implemented with only the phase section current and optical frequency deviation of 20 GHz is achieved. The measured frequency modulation response indicates the limitation of the maximum modulation rate is sufficient for Internet protocol packet optical labeling.

This paper improves the performance of contribution of photovoltaic (PV) panels to the stabilization of a standalone hybrid microgrid during frequency excursions. Recent growth in converter-interfaced topologies and using renewable energy resources has led to decrease in inertia of isolated micro-grids (MGs) and presented some challenges to the frequency control and strength of hybrid ones. One of the effective measures to resolve mentioned problems is to deploy some potential energy resources like PV by considering a small proportion of solar panels' generation as headroom. In addition, other distributed energy resources (DERs), which can be regarded as ancillary resources, can play an active role in the resiliency of MG's performance in the low-inertia power systems. To this effective contribution considering some uncertainties of the microgrid parameters like the time constant of the micro-turbine, the time constant of the governor, the speed droop regulation constant as well as the load damping coefficient, an adaptive fuzzy mechanism can be proposed to determine the level of active power injection. Recurrent Adaptive Neuro Fuzzy Inference System (ANFIS) technique trains this nonlinear adaptable droop to deal with uncertainties in a reasonable way. In order to ascertain parameters of membership functions in an intelligent way, this droop takes advantage of Artificial Bee Colony (ABC) algorithm based on a multi-objective. The Simulation results verify the robustness and reliability of this flexible fuzzy droop during different operating conditions as well as intermittent nature of renewable energy resources such as wind turbine generators in hybrid micro-grid.

Combined cycle gas turbines (CCGTs) have considerable merits and are mainly the most frequently researched topics in power generation, due to their attractive performance characteristics and low-emission combustion system. A change in power demands throughout a power system is reflected by a change in the frequency in the network. Therefore, a significant loss in a power system without a suitable control system can cause an extreme frequency disturbance in the network. However, it has been observed that many research studies have focused on control issues and voltage stability, contrary to frequency control, which receives less interest in this field. Considering these remarks, our contribution deals with the frequency control system. This study focuses on the effectiveness of the frequency control system in a combined cycle gas turbine plant. Thus, a dynamic model for a CCGT plant has been developed in MATLAB/Simulink, and the power system responses are examined following the frequency deviation.

Frequency control represents a critically significant issue for the enhancement of the dynamic performance of isolated micro grids. The micro grid system studied here was a wind–diesel system. A new and robust optimization technique called the mine blast algorithm (MBA) was designed for tuning the PID (proportional–integral–differential) gains of the blade pitch controller of the wind turbine side and the gains of the superconducting magnetic energy storage (SMES) controller. SMES was implemented to release and absorb active power quickly in order to achieve a balance between generation and load power, and thereby control system frequency. The minimization of frequency and output wind power deviations were considered as objective functions for the PID controller of the wind turbine, and the diesel frequency and power deviations were used as objective functions for optimizing the SMES controller gains. Different case studies were considered by applying disturbances in input wind, loa...

The increasing level of wind power integration using doubly fed induction generators (DFIGs) has implications for local frequency support as a consequence of decoupling between DFIG rotor speed and grid frequency. To ensure reliable and stable power system operation with DFIG integration, supplementary inertial control strategies are required. Conventional inertial control algorithms which use the rate of change of frequency (RoCoF) and frequency deviation loops (droop loop) require great effort to determine appropriate gains suitable for all power grid and wind speeds. In this paper, the influence of supplementary inertial control loop parameters on the inertial response and power system frequency are analysed. An active control strategy is proposed for frequency regulation using variable gains in the frequency deviation loop for the inertial controller. The variable gain control approach is shown to actively respond to system changes to improve the performance. The controller is compared with the widely used PID method. The proposed method is shown to enhance the frequency nadir and guarantee steady DFIG operation.

The rapid expansion of energy infrastructure in emerging economies, particularly in India and Africa, necessitates advanced control and computational strategies to ensure the seamless integration of green energy resources with conventional power systems. This study conducts a comprehensive analysis of state-of-the-art control mechanisms and optimization techniques for hybrid power networks, focusing on enhancing grid stability, frequency regulation, and resilience under dynamic loading and climatic variations. It explores advanced generation control strategies, including adaptive and predictive control frameworks, to mitigate the inherent intermittency of renewable energy sources. Furthermore, the paper examines multi-objective optimization methodologies for energy dispatch, frequency stabilization, and reliability enhancement in multi-entity power networks. By proposing a robust and computationally efficient framework for hybrid energy integration, this study contributes to the development of resilient, self-sustaining power systems crucial for ensuring longterm energy security, operational efficiency, and economic growth in rapidly developing regions.

We present and evaluate a lightweight and effective approach for the management of prosumer communities through the synergistic control of the power electronic converters acting therein. These controllable elements are the utility interface (UI), installed at the point of common coupling with the electrical utility, and the energy gateways (EGs), interfacing distributed generation units and energy storage devices with the distribution grid. The UI acts as the control master for the microgrid, collecting information on generators and power demand and dispatching a control parameter that regulates both energy storage devices and generators. An islanded operation mode is considered, and the control strategy aims at leveling peaks in the energy drained from or injected into the UI. The proposed control strategy is tested on a residential microgrid model, 100 kVA rated, which has been developed and utilized to analyze selected performance metrics in the presence of realistic and time varying power demand and energy generation processes. This model allows a fine-grained analysis of a) the energy storage state at each residential unit, b) the amount of energy required at the UI, and c) the locally generated energy injected into the micro grid by each EG. As a further result, our framework returns the amount of distributed storage required to achieve a target peak-shaving performance level, according to the number of residential users with generation capability and their storage capacity.

This paper derives two kinds of Q factor formulas for use in estimating frequency stability of sinusoidal oscillators. To cover various circuit topologies, we consider a general black-box model having DC input and RF output ports. Assuming a small deviation in DC supply voltage or RF load impedance, the frequency shift caused by such deviation is formulated by a perturbation technique. As a versatile criterion to indicate the tolerance of circuit itself, we introduce oscillator Q against each deviation. Since the theory is described in the linear impedance domain, it gives oscillator designers a clear vista on the stability of practical circuits.

The increased penetration of renewable energy sources (RES) and electric vehicles (EVs) is resulting in significant challenges to the stability, reliability, and resiliency of the electrical grid due to the intermittency nature of RES and uncertainty of charging demands of EVs. There is a potential for significant economic returns to use vehicle-to-grid (V2G) technology for peak load reduction and frequency control. To verify the effectiveness of the V2G-based frequency control in a microgrid, modeling and simulations of single- and multi-vehicle-based primary and secondary frequency controls were conducted to utilize the integrated components at the Canadian Centre for Housing Technology (CCHT)-V2G testing facility by using MATLAB/Simulink. A single-vehicle-based model was validated by comparing empirical testing and simulations of primary and secondary frequency controls. The validated conceptual model was then applied for dynamic phasor simulations of multi-vehicle-based frequenc...

Load frequency control (LFC) is a crucial feature of electric power systems to maintain a balance between power supply and load demand, thus avoiding a deviation of the grid frequency. The present work aims to implement an effective LFC scheme for a microgrid system consisting of a diesel generator (DEG), a wind turbine generator (WTG) and a battery storage system. Proportional-integral-double-derivative (PIDD) controllers are used to implement the proposed LFC scheme. The controller parameters are computed using an innovative hybrid teaching-learning-optimization differential-evaluation (hTLO-DE) algorithm. The main scope of the work lies in application of hTLO-DE optimized PIDD controllers in DEG-WTG-battery storage based MG system. The results obtained with PIDD controllers are compared with those obtained with the traditional PI and PID controllers. A critical analysis shows that the PIDD controller can provide better dynamic responses in terms of settling time and magnitude of ...

Frequency deviations and inter-area tie-power fluctuations from their respective scheduled values following a local load disturbance are a source of great concern in interconnected power system operation and control. In this paper, a new method is proposed to minimise such deviations and thereby enhance the performance of Automatic Generation Control (AGC) of an interconnected hydrothermal power system with the coordinated operation of a Superconducting Magnetic Energy Storage (SMES) unit in thermal area and a Thyristor Controlled Phase Shifter (TCPS) in series with the tie-line. Generation Rate Constraints (GRCs) are also considered for the thermal and hydro systems. The integral controller gain settings are obtained by optimising a quadratic performance index using the Integral Squared Error technique. Comparison of the dynamic responses demonstrates the effectiveness and efficiency of the suggested SMES-TCPS combination in suppressing and stabilising the oscillations of frequency and tie-power deviations as well as in reducing the settling time.

Owing to the various sources of complexity in the electrical power system, such as integrating intermittent renewable energy resources and widely spread nonlinear power system components, which result in sudden changes in the power system operating conditions, the conventional PID controller fails to track such dynamic challenges to mitigate the frequency deviation problem. Thus, in this paper, a fuzzy PI controller is proposed to enhance the automatic generation control system (AGC) against step disturbance, dynamic disturbance, and wind energy disturbance in a single area system. The proposed controller is initialized by using Equilibrium Optimization and proved its superiority through comparison with a classical PI optimized base. Results show that the fuzzy PI controller can reduce the peak-to-peak deviation in the frequency by 30–59% under wind disturbance, compared to a classical PI optimized base. Moreover, a fuzzy PID controller is also proposed and EO initialized in this pa...

Power systems have recently faced significant challenges due to the increased penetration of renewable energy sources (RES) such as frequency deviation due to fluctuations, unpredictable nature, and uncertainty of this RES. In this paper, a cascaded controller called (1+PD)-PID is proposed to reduce the influence of RES uncertainties on the system and to maintain the system's reliability during fluctuations. The proposed controller is a combination of (1+PD) and PID controllers in order. The output signal of the (1+PD) controller along with the frequency deviation and the power difference between adjacent areas are used as inputs to the PID controller to create the load reference signal. The parameters of the suggested controller are optimally tuned using the African Vulture Optimization Algorithm (AVOA) to ensure the best performance of the controller. A two-area interconnected system with non-reheat thermal power units combined with RES such as solar and wind energy is modeled using MATLAB/Simulink to evaluate the system response. The controller effectiveness is verified by subjecting the studied system to various types of fluctuations such as step load disturbance, variable load perturbation and RES penetration. The obtained simulation results prove that the proposed (1+PD)-PID controller in integration with AVOA offers a significant improvement in the system performance specifications. Moreover, the proposed AVOA-based (1+PD)-PID controller has proven its superiority over other comparable controllers having the least fitness function of 6.01 × 10 -5 .