Automatic Generation Control Problem Research Articles

Fuzzy analytic hierarchy process based generation management for interconnected power system

Decision makers consistently face the challenge of simultaneously assessing numerous attributes, determining their respective importance, and selecting an appropriate method for calculating their weights. This article addresses the problem of automatic generation control (AGC) in a two area power system (2-APS) by proposing fuzzy analytic hierarchy process (FAHP), an multi-attribute decision-making (MADM) technique, to determine weights for sub-objective functions. The integral-time-absolute-errors (ITAE) of tie-line power fluctuation, frequency deviations and area control errors, are defined as the sub-objectives. Each of these is given a weight by the FAHP method, which then combines them into an single final objective function. This objective function is then used to design a PID controller. To improve the optimization of the objective function, the Jaya optimization algorithm (JOA) is used in conjunction with other optimization techniques such as sine cosine algorithm (SCA), Luus–Jaakola algorithm (LJA), Nelder–Mead simplex algorithm (NMSA), symbiotic organism search algorithm (SOSA) and elephant herding optimization algorithm (EHOA). Six distinct experimental cases are conducted to evaluate the controller’s performance under various load conditions, with data plotted to show responses corresponding to fluctuations in frequency and tie-line exchange. Furthermore, statistical analysis is performed to gain a better understanding of the effectiveness of the JOA-based PID controller. For non-parametric evaluation, Friedman rank test is also used to validate the performance of the proposed JOA-based controller.

Application of an intelligent fuzzy logic based sliding mode controller for frequency stability analysis in a deregulated power system using OPAL-RT platform

Owing to unpredicted power demand, integration of distributed generators and parametric disparity in the system cause the power system more intricacy which in turn affects the frequency and power fluctuation critically. To mitigate these phenomena, an intelligent and efficient automatic generation control (AGC) is indispensable. Therefore, in this paper, an intelligent and robust controller named fuzzy sliding mode controller (FSMC) has been recommended to tackle the AGC problem in a deregulated power system in the presence of distributed generators effectively. Again, to manifest its dominance over SMC, fuzzy-PID and PID controllers, dynamic response of the deregulated system under Poolco, Bilateral and Contract violation conditions has been evaluated and compared. Further, to support the FSMC controller’s ability, another test model has been taken for the AGC study. The gains of these controllers have been enumerated by Gannet Optimization Algorithm (GOA) subjecting to a minimization problem. Exploration phase of GOA involves two steps, namely U- & V-shaped diving patterns of Gannets and exploitation phase again involves two steps namely sudden rotation and random walk of Gannets. These four steps ensures quick convergence of the objective function to its optimal value. Apart from transient response under different power transaction conditions, parametric uncertainties of the system, solar and wind power variation and abrupt load change have been considered to support the robustness & credibility of FSMC controller. In addition to this, the stability of the proposed model has been analyzed in the frequency domain. Finally, the MATLAB/SIMULINK based transient response has been validated by a real-time simulator using OPAL-RT-4510, Xilinx Kintex-7 FPGA software.

A novel chaotic chimp sine cosine algorithm part-II: Automatic generation control of complex power system

This is part II of two-paper set deals with the application of the designed algorithm in automatic generation control (AGC) problem of different structures, such as conventional and isolated. The companion paper (i.e., Part I) describes the designed algorithm in detail and shows the validation of the results in support of the proposed algorithm in a systematic manner. The main focus of this study is to apply and validate the algorithm for automatic generation control (AGC) in power systems. Specifically, the algorithm is tested on various AGC models, including a conventional four-area interconnected power system, deregulated power system and an isolated power system model withchange in operating conditions. The employed controller is a proportional-integral-derivative (PID) controller, and its parameters are tuned using the chimp optimization algorithm (CHOA), chaotic chimp optimization algorithm (C-CHOA), and chaotic chimp sine cosine optimization algorithm (C-CHOA-SC).The objective of this study is to assess the effectiveness of the proposed C-CHOA-SC optimization method in tuning the PID controller parameters for AGC in the considered power system models. Through simulations, the study evaluates the performance of the algorithm in terms of AGC efficiency and the ability to maintain dynamic responses within acceptable limits.The simulation results demonstrate that the proposed algorithm outperforms alternative methods in all the tested AGC models. It showcases superior performance while ensuring the desired dynamic response characteristics are maintained.

Influence of ultra‐capacitor on AGC of five-area hybrid power system with multi-type generations utilizing sine cosine adopted dingo optimization algorithm

In this article, the load frequency control (LFC) problem of a five-area automatic generation control (AGC) power system with multi-type generations is addressed by utilising a technique that considers the changing of the dingo optimisation algorithm known as sine cosine adopted dingo optimisation algorithm (SCaDOA) based tilt integral derivative (TID) regulator. AGC needs an energy storage system (ESS) and some intelligent adaptable control techniques to guarantee the balance in the system's stability. Consequently, this paper utilizes the TID regulator alongside ultra‐capacitor (UC) ESS to settle the AGC issue. At first, the execution of the sine cosine adopted calculation is tried by contrasting it and the standard DOA calculation by considering different standard benchmark functions. Further, the proposed plan strategy is likewise applied to a five-area system comprising different conventional and non-conventional/renewable energy sources along with UC. The execution of the controller has been finished for different stacking conditions of the power system. It is seen that the said UC with TID controller is more viable contrasted with the other standard controllers.

Liberalized automatic generation control of interconnected thermal‐hydro‐gas system using firefly algorithm optimized PID controller

AbstractIn liberalized power system, the distribution companies are free to choose the contracts with all available generating companies (GENCOs), while the independent system operator acting as regulatory authority. In this article, the liberalized automatic generation control problem is studied for a two‐area interconnected power system. The studies are divided into two parts. In first part, the investigations are carried out with only thermal generations in each area. In second part, the multi‐diverse sourced thermal‐hydro‐gas GENCOs are assumed in each control area. For damping out the system frequency and tie‐line power dynamics, the popularly known PI and PID controllers are employed considering poolco, bilateral and contract violation transactions. In both the parts, the comparative performance between PI/PID controllers is made. Studies reveal that PID outperforms PI controller. A case study with introduction of time delay in the system is performed as the open communication infrastructure introduces inherent time delay in the smart grid scenario. To understand the impact of frequency sensitive loads on system dynamics, the effect of variation in damping coefficient (D) is studied and it is shown how the D is influencing the dynamics. The effect of double reheat turbines is also studied and found that they deteriorate the dynamics when compared to single reheat turbines. In all the above studies, the powerful nature inspired firefly algorithm is employed to optimize the parameters of controllers using the integral squared error method.

Mitigation of AGC Problem of the RES Integrated Hydro-Thermal System Using FACTS and INEC Based AHVDC with ESS Considering the 3DOF-TIDN Controller

This article highlights the mitigation of automatic generation control (AGC) problem considering various FACTS devices and inertia emulation control (INEC) strategies based accurate model of HVDC links using various energy storage systems (ESS). The proposed system comprises a three-area hydro-thermal system integrated with renewable energy sources (RES) such as a realistic dish-Stirling solar thermal system (RDSTS) and a precise wind turbine system (PWTS). A maiden effort has been made to design a novel secondary controller called three degrees of freedom tilt-integral-derivative with filter coefficient (3DOF-TIDN) for the proposed system, and their parameters are successfully optimized by the bird swarm algorithm (BSA). The comparative analysis of system dynamics corresponding to the proposed 3DOF-TIDN controller exhibits betters dynamics over 3DOF-PID and 2DOF-PID controllers. The system dynamic performances are also evaluated considering RDSTS and PWTS in all the areas, and it has been evident that the incorporation of RDSTS and PWTS improves system dynamics. The system with AHVDC link shows quickly damped out the system oscillation. Moreover, the study on inertia emulation using various ESS such as redox flow battery (RFB), ultra-capacitor (UC) and battery energy storage (BES) systems inferred that the system’s initial response with RFB is better than others. Furthermore, the integration of various FACTS devices such as thyristor control phase shifter (TCPS), static synchronous series capacitor (SSSC) and Gate-Controlled Series Capacitor (GCSC) revealed that the system dynamics marginally enhanced with GCSC than TCPS and SSSC.

Data-Driven Automatic Generation Control of Interconnected Power Grids Subject to Deception Attacks

In this paper, a Data-Driven Adaptive Control (DDAC) technique is proposed for the Automatic Generation Control (AGC) problem of an interconnected power grid subject to Deception Attack (DA). The emergence of the Internet of Things (IoT) and the advancement of communication technologies have provided an opportunity for power system operators and designers to compensate for the lack of an appropriate model using a huge amount of data. However, they have also caused security challenges in the grid due to malicious attackers. This paper focuses on the attack to the control network which carries the AGC signals between the secondary and local primary frequency controllers. Intentional modifications of AGC signals during an attack may result in frequency instability because of saturation in governor signals. To counteract such an attack, a DDAC is suggested for a multi-area power system in which, the system model is dynamically updated using real-time input and output signals. The model includes the attacker’s behavior, thus empowering the control system to act against it. The stability of the proposed controller is proved using the Lyapunov stability theory when the DA causes input saturation. Simulation results show that it can successfully tolerate a class of deception attacks and keep the multi-area power grid stable.

Intelligent priority based generation control for multi area system

ABSTRACT Increased innovation and automation for higher comfort level of living beings have stressed power sector for more units of power to be generated and supplied. To meet this demand, several generating stations are needed to be connected to supply this enormous load to end consumers. While interconnecting generated power of multi-area system, several problems are being encountered. In the current proposed work, 2-area interconnected power system has been considered and automatic generation control problem for single area loading and multi-area multi-type loading has been solved with the help of intelligent control strategy using TLBO algorithm. The proposed work has been processed and simulated in MATLAB and SIMULINK environment. Three types of loadings are considered in the proposed work. First one is single area fixed loading, second one is both area loading with different fixed load, and third one is increasing type load for fixed duration of time in single area. Based on the nature of severity of disturbances in power network, a fitness function has been designed for these multi-type of loadings to improve its transient response to avoid failure of synchronism and improve resiliency of power network to supply uninterrupted power to end consumers.

Discrete optimal quadratic AGC based cost functional minimization for interconnected power systems

The increasing complexity and difficulty of the Automatic generation control (AGC) problem has resulted from the increasing scale of interconnected power networks and changing daily demands. The primary goals of AGC are to control frequency variations at nominal levels and tie-line power variances at planned levels. To effectively deal with AGC control difficulties, this study introduces Discrete Optimal Quadratic Automatic Generation Control (OQAGC). One advantages of this method is the differentiation of quadratic cost function results into linear terms while minimizing control actions and minimizing state deviations. This developed control method leads to a simple and easy discrete control law that can be implemented for both linear and nonlinear systems. For optimizing the controller, this research work utilized an optimum control theorem using Lagrangian multipliers, while the functional minimization technique is used for systematically selecting the state and control weighting matrices in discrete form for N control regions (where N is the number of interconnected power systems). The discrete cost function needs are derived using this technique in terms of area control errors, integral area control errors, and control energy expenditure. Four interconnected power systems were analyzed with/without disturbances and area control errors, each with one thermal, hydro, and gas-generating unit. A two-area multi-source power system with renewable energy in control area 2 is analyzed for the performance of the proposed controller with generation rate constraints (GRCs). The functional minimization technique simplifies and eases the choosing of weighting matrices. Furthermore, the simulation findings suggest that the developed discrete optimum quadratic AGC control-based cost functional minimization approach enhances power system dynamics in terms of stability, steady-state performance, and the closed-loop control system's robustness to input load disturbances. As a result, the newly developed OQAGC approach demonstrates the significance of the discrete LQR controller for N multi-area power systems.

Combined frequency and voltage regulation in multi-area system using an equilibrium optimiser based non-integer controller with penetration of electric vehicles

The frequency and terminal voltages in power systems are the two critical operating points that balance active and reactive powers, which must be maintained within predefined limits through automatic generation control (AGC) and automatic voltage regulator (AVR) control loops. To address this issue, in this manuscript, the problem of a combined AGC and AVR is solved using a non-integer order PID controller. Its parameters are adjusted using the recently proposed equilibrium optimiser (EO) technique. Along with conventional sources, renewable energy sources such as geothermal power plants and electric vehicles are considered. The EO-tuned non-integer order PID controller outperformed the traditional integer order PI and PID controllers in view of peak overshoots, undershoots and settling times. The performance of EO is evaluated and found superior against widely used particle swarm optimisation and firefly algorithms in the presence of non-integer order PID controller. Different case studies such as random load disturbance, the effect of nonlinearities, the impact of communication time delay and sensitivity analysis have been performed, which also test the heftiness of the non-integer order PID controller. The obtained results are verified using the real-time digital simulator OPAL-RT (OP4510) for nominal conditions.

SMES-GCSC Coordination for Frequency and Voltage Regulation in a Multi-Area and Multi-Source Power System with Penetration of Electric Vehicles and Renewable Energy Sources

Frequency, tie-line power, and the terminal voltages of synchronized generators must all be kept within prescribed limits to ensure the stability of an interconnected power grid through combined automatic generation control (AGC) and automatic voltage regulator (AVR) loops. Thermal power plants, electric vehicles, and renewable energy sources—including solar and wind, geothermal, and solar thermal power plants—form the two-area integrated power system in present research. A new cascade controller named the cascaded proportional integral derivative (PID) and fractional-order PID (CPID-FOPID) controller is proposed for the first time, whose performance is compared with the PID and FOPID controller. The results show that the proposed cascade controller outperforms PID and FOPID in delivering superior dynamic characteristics, including short settling times and low oscillation amplitudes. A new metaheuristic algorithm named the coot algorithm was applied to optimize the parameters of these controllers. The suggested controller outperforms FOPID in the combined AGC and AVR problem under uncertain conditions (random load disturbance, variable input of solar irradiation, and wind power). Robustness of the controller is tested with significant variation in the turbine time constant of the thermal and geothermal power plant. In this study, authors also investigated the best possible coordination between the superconducting magnetic energy storage (SMES) and gate-controlled series capacitor (GCSC) devices to control both voltage and frequency simultaneously. The effect of communication time to the power system is analyzed in this study. Additionally, the obtained results are satisfactorily validated using OPAL-RT real-time digital simulator.

A Particle Swarm Optimization Technique Tuned TID Controller for Frequency and Voltage Regulation with Penetration of Electric Vehicles and Distributed Generations

An interconnected power system requires specific restrictions to be maintained for frequency, tie-line power, and the terminal voltage of synchronized generators to avoid instability. Therefore, frequency stability and voltage regulation issues are covered individually and jointly in the current research work. Initially in test system 1, automatic generation control (AGC) investigations are done on two interconnected systems with thermal plants and electric vehicles in one area and distributed generation and electric vehicles in other area. The automatic voltage regulator (AVR) problem alone is chosen for investigation in test system 2. The third test system addresses the combined AGC and AVR issues. The performance of the fractional-order tilt-integral-derivative (TID) controller is compared with that of a widely used proportional integral derivative (PID) controller in all three test systems studies. The findings demonstrate better performance of the TID controller than PID in terms of providing superior dynamic metrics, such as low peak overshoots, undershoots, and settling time, as well as decreased oscillations amplitudes. Additionally, TID performs better than PID despite randomized load disturbance, system non-linearities, and time delays in AGC and the combined AGC and AVR problem. The PSO-tuned TID controller is insensitive to variation in load damping factor and time constants of the AVR system. Finally, the results are validated by an OPAL-RT 4510 real-time digital simulator.

NEURO-FUZZY INTELLIGENT CONTROLLER FOR LFC OF A FOUR-AREA POWER SYSTEM

In modern complex power systems, the problem of automatic generation control arises due to a sudden increase or decrease in load. This problem leads to instability in the system if the frequency control is not automatic, which may finally lead to system collapse. Hence, automatic control of frequency and tie-line power is significant. This research paper develops and compares the performance of an adaptive neuro-fuzzy inference system (ANFIS) controller with the conventional PID controller and the Takagi-Sugeno-Kang fuzzy logic controller for load frequency control (LFC) of a four-area power system with generation rate constraint (GRC) on turbines. The performance is compared in terms of errors, settling time and maximum undershoot of the frequency deviation for different step load changes using Matlab. The proposed ANFIS controller performs with less peak undershoot of - 0.7374 Hz and a settling time of 27.9823 sec at a 4% change in load. It reduces the steady-state error to zero. Thus, the proposed controller is the most suitable LFC in energy centers. The system parameters are taken from the IEEE press, and EPRI published books.

FACTS-based 3DOF-PID Controller for LFC of Renewable Power System Under Deregulation Using GOA

ABSTRACT This article introduces grasshopper optimization algorithm (GOA) for getting an optimum and effective solution of an automatic generation control (AGC) problem in hybrid power systems under a deregulated scenario using a 3-degree freedom of proportional integral and derivative (3DOF-PID) controller. Initially, two-area thermal-hydro-gas-wind system is considered. To compensate the load disturbances and to stabilize the area frequency oscillations, dynamic power flow control with PID and 3DOF-PID controllers have been studied. The performances of PID and 3DOF-PID controllers are evaluated with high voltage direct current (HVDC) link. The present investigation reveals that GOA-optimized 3DOF-PID controller is better than any other conventional technique, such as moth swarm algorithm (MSA) optimization technique. Afterward, a two-area AGC system is considered, where in one area wind power plant is incorporated with a thermal hydro plant and in the other area solar thermal power-plant (STPP) is incorporated with the same combination. Additionally, the effectiveness of different flexible AC transmission system (FACTS) devices e.g. unified power flow controller (UPFC) and super conducting magnetic energy storage device (SMES) along with 3DOF-PID controller-based AGC is compared with PID controller-based AGC. Moreover, to make the AGC problem more realistic, initially generation rate constraints (GRC) and governor with dead band (GDB) and finally boiler dynamics (BD) are included in thermal unit, and performance of the proposed controller using proposed algorithm is evaluated under deregulated environment. Finally, the proposed technique is applied under random load variation.

An intelligent two‐level control of Ultrabattery for improved automatic generation control of a multi‐source deregulated power system

This article studies automatic generation control (AGC) problem of a realistic, multi-source deregulated power system with thyristor controlled phase shifter (TCPS) in the tie-line for damping the inter-area power oscillations. In this direction, an intelligent integration of a commercially available, relatively low rating efficient energy storage system in the form of Ultrabattery for AGC enhancement is proposed. Herein, a two-level supervisory cascade control scheme is designed. The supervisory controller comprises of a genetically tuned fuzzy logic controller which ensures the operation of Ultrabattery within its regime of operation while supervising a conventional main controller in the inner loop. The exchange of desired power between the power system and Ultrabattery is governed by the main controller whose parameters are tuned offline using optimization toolbox in MATLAB. To make the power system model realistic, effects of generation rate constraint, governor dead band, boiler dynamics, and communication time delay have been incorporated. Extensive simulation studies conducted on a well-known benchmark two-area power system under various market scenarios demonstrate the effectiveness of the proposed scheme. The results obtained illustrate a substantial mitigation of low magnitude oscillations with TCPS in the tie-line. Following the introduction of an Ultrabattery rated at just 1% of the area capacity in coordination with TCPS, an improvement of at least 87% in the system performance index is achieved. Finally, a sensitivity analysis validates the robustness of the proposed scheme under varying system parameters and market scenarios.

Design and analysis of search group algorithm-based PD-PID controller plus redox flow battery for automatic generation control problem

Design and analysis of search group algorithm-based PD-PID controller plus redox flow battery for automatic generation control problem

Design and analysis of search group algorithm-based PD-PID controller plus redox flow battery for automatic generation control problem

Design and analysis of search group algorithm-based PD-PID controller plus redox flow battery for automatic generation control problem

Pathfinder algorithm optimized fractional order tilt-integral-derivative (FOTID) controller for automatic generation control of multi-source power system

This paper introduces a fractional order tilt-integral-derivative (FOTID) controller which is structurally analogous to fractional order proportional-integral-derivative controller in a power system for solving automatic generation control (AGC) problem. It is optimized by a recent metaheuristic optimizer called pathfinder algorithm (PFA). An interconnected two-area power system model comprising of multi-sources like thermal, hydro and gas generating units including physical constraints namely, governor dead band (GDB) and generation rate constraint (GRC) are taken into consideration for the study. The efficiency of the proposed controller for AGC is shown by comparing it with PFA optimized tilt-integral-derivative (TID) and proportional-integral-derivative (PID) controllers with integral of time multiplied absolute error (ITAE) taken as the objective function. Simulation study supports the claim that the proposed controller provides better dynamic responses as compared to the others. Sensitivity and robust analyses are done to demonstrate the effectiveness of the proposed PFA optimized FOTID controller to a wide variation in system parameters, at different step load and random load disturbances.

GA Technique to Solve the AGC Problem of Thermal Generating Unit

In a large-scale power system, Automatic Generation Control (AGC) is responsible to area load changes and abnormal imprecise system operating parameters essentially means very fast minimization of area frequency deviations and mutual tie line power flow deviations of the areas for satisfactory and stable operation of the system. Some technique gives the good results in normal operation but in abnormal condition, it take large time to settle down the load disturbance, which is harmful for the system. New approach (Genetic Algorithm (GA) Technique) provides better control performance over frequency deviations and tie line power flow deviations due to a normal and abnormal operating condition of sudden load changes. In this paper six area model of thermal generating units has been developed and simulated in MATLAB Simulink software. Response of the developed model has been obtained by GA technique and compared with the other technique like; fuzzy, PID. Tabulated result shows that the GA technique give the better performance over the other technique due to settle down the frequency and tie line power flow deviations in less time and maintain the system constancy.

Meta-Heuristic Algorithm Optimized Fuzzy PID Controlled AGC of Three Area Power System

The problem of automatic generation control (AGC) is a major concern in power utilities; it plays a major role of the complicated structure and dimension of the multi-area systems. Automatic Generation Control's main intention in the multi-area system is to maintain the frequency of each control area and remain the tie-line power flows within the many defined tolerance limits by modifying the Automatic Generation Control generators’ actual power outputs to accommodate the changing load requirements. Frequency control is accomplished through the primary control mechanism or the governor control mechanism. But the Area Control Error (ACE) always present in the system. The secondary controllers are surmounting this ACE to zero. The design tunes the controllers to enhance the better dynamic performance and stability of these eccentric conditions. The goal of this work is to diminish area control error, settle time, under-shoots and over-shoots of frequency divergence and net interchange tie-line error. Generally the gain values of the PID Control parameters obtain by tribulation and error technique and it need additional computation time. To reduce this obscurity of tuning of PID gains Evolutionary algorithm approach can be habituated to optimize the PID gains. Fuzzy – PID have been employed with different objective to enhance the efficient optimal solutions to the three area system. In this proposed study, GWO technique used to maximize Fuzzy-based PID controller's Proportional, Integral and Derivative gains in Three Area System.