Automatic Voltage Regulator System Research Articles

Modified and Improved TID Controller for Automatic Voltage Regulator Systems

This paper proposes a fractional order integral-derivative plus second-order derivative with low-pass filters and a tilt controller called IλDND2N2-T to improve the control performance of an automatic voltage regulator (AVR). In this study, equilibrium optimisation (EO), multiverse optimisation (MVO), and particle swarm optimisation (PSO) algorithms are used to optimise the parameters of the proposed controller and statistical tests are performed with the data obtained from the application of these three algorithms to the AVR problem. Afterwards, the performance of the IλDND2N2-T controller is demonstrated by comparing the transient responses with the results obtained in recently published papers. In addition, extra disturbances such as frequency deviation, load variation, and short circuit faults in the generator are applied to the AVR system. The proposed controller has outperformed the compared controller against these disturbances. Finally, a robustness test is performed in terms of deterioration in the system parameters. The results show that the IλDND2N2-T controller outperforms the compared controllers under all conditions and exhibits a robust effect on the perturbed system parameters.

Assessment of the performance and robustness of a new PID tuning technique for AVR systems

ABSTRACT In this work, a hybrid PID controller, combining Zeigler–Nichols (ZN) theory and the dominant pole placement, has been developed for the automatic voltage regulator (AVR) systems. Furthermore, the reduced order models (ROMs) of AVR systems, namely, AVR-1, AVR-2, and AVR-3, have been established using Particle Swarm Optimization (PSO), Big Bang Big Crunch (BBBC), and Pade’s approximation, respectively. Then, the proposed PID controller has been applied to both the original and developed lower order models of AVR to check the efficacy and robustness of the developed hybrid controller. It is shown that the proposed hybrid PID controller provides a very small value of the percentage peak overshoot (%M p ) and settling time (t s ). Also, the gain margin (GM) and phase margin (PM) are found to be in the desired range. Finally, the sensitivity analysis has also been performed to check the robustness of the proposed controller. The sensitivity function, complementary sensitivity function, and various performance indices have been evaluated for the proposed controller based on Bode’s Integral (BI), Tyreus–Lyuben (T–L), and Internal Model Control (IMC) approaches. The comparative studies of the results show that the proposed controller has smaller values of performance indices and sensitivity function.

Q-Learning-Based Dumbo Octopus Algorithm for Parameter Tuning of Fractional-Order PID Controller for AVR Systems

The tuning of fractional-order proportional-integral-derivative (FOPID) controllers for automatic voltage regulator (AVR) systems presents a complex challenge, necessitating the solution of real-order integral and differential equations. This study introduces the Dumbo Octopus Algorithm (DOA), a novel metaheuristic inspired by machine learning with animal behaviors, as an innovative approach to address this issue. For the first time, the DOA is invented and employed to optimize FOPID parameters, and its performance is rigorously evaluated against 11 existing metaheuristic algorithms using 23 classical benchmark functions and CEC2019 test sets. The evaluation includes a comprehensive quantitative analysis and qualitative analysis. Statistical significance was assessed using the Friedman’s test, highlighting the superior performance of the DOA compared to competing algorithms. To further validate its effectiveness, the DOA was applied to the FOPID parameter tuning of an AVR system, demonstrating exceptional performance in practical engineering applications. The results indicate that the DOA outperforms other algorithms in terms of convergence accuracy, robustness, and practical problem-solving capability. This establishes the DOA as a superior and promising solution for complex optimization problems, offering significant advancements in the tuning of FOPID for AVR systems.

Optimal control of automatic voltage regulator system using hybrid PSO-GWO algorithm-based PID controller

Optimal control of automatic voltage regulator system using hybrid PSO-GWO algorithm-based PID controller

Optimizing PID controller parameters for robust automatic voltage regulator system through indirect design approach-2

Automatic voltage regulators (AVR) are designed to manipulate a synchronous generator’s voltage level automatically. Proportional integral derivative (PID) controllers are typically used in AVR systems to regulate voltage. Although advanced PID tuning methods have been proposed, the actual voltage response differs from the theoretical predictions due to modeling errors and system uncertainties. This requires continuous fine tuning of the PID parameters. However, manual adjustment of these parameters can compromise the stability and robustness of the AVR system. This study focuses on the online self-tuning of PID controllers called indirect design approach-2 (IDA-2) in AVR systems while preserving robustness. In particular, we indirectly tune the PID controller by shifting the frequency response. The new PID parameters depend on the frequency-shifting constant and the previously optimized PID parameters. Adjusting the frequency-shifting constant modifies all the PID parameters simultaneously, thereby improving the control performance and robustness. We evaluate the robustness of the proposed online PID tuning method by comparing the gain margins (GMs) and phase margins (PMs) with previously optimized PID parameters during parameter uncertainties. The proposed method is further evaluated in terms of disturbance rejection, measurement noise, and frequency response analysis during parameter uncertainty calculations against existing methods. Simulations show that the proposed method significantly improves the robustness of the controller in the AVR system. In summary, online self-tuning enables automated PID parameter adjustment in an AVR system, while maintaining stability and robustness.

Robust variable-order fractional PID-LP fuzzy controller for Automatic Voltage Regulator systems

Precise and robust Automatic Voltage Regulator (AVR) control is essential for maintaining a stable voltage output from a synchronous generator under varying operating conditions. This study aims to improve transient response and robustness characteristics of AVR systems by developing a novel fuzzy-based variable-order fractional PID-LP control scheme. To achieve this goal, in the first step, a novel fractional-order PID-LP controller tuned with a hybrid Particle Swarm Optimization algorithm is designed. This controller outperforms current fractional- and integer-order PID controllers in terms of performance and transient response. To further improve robustness against changing conditions in power system dynamics, in the second step, a fuzzy-based variable-order fractional PID-LP controller is developed to adaptively adjust the controller’s parameters based on error and its variations. The results demonstrate that the proposed technique surpasses recently proposed methods in terms of performance indexes, peak overshoot, rise time, and settling time. An extensive analysis is also conducted to compare the new approach with other methods in improving stability, robustness, trajectory tracking, rejecting disturbances, and handling nonlinearities and sensor delays. The results confirm the applicability and superiority of the controller in maintaining stability under fluctuating power system dynamics with up to ±50% variations.

Automatic Voltage Regulator Control System for Synchronous Generator

Presented in this paper is Automatic Voltage Regulator (AVR) Control System for Synchronous Generator. A nonlinear model of synchronous generator was developed in Simulink and was later linearized as a single input single output (SISO) transfer function model. A Proportional Integral and Derivative (PID) controller was designed and by using the MATLAB/Simulink PID tuner, the gains of the proportional, integral and derivative parameters were obtained. A Low Pass Filter, F(s) was designed and introduced as part of the input signal to eliminate any noise effect that may be introduced into the AVR control system through the input. Simulations were carried out considering basically three scenarios viz: the AVR control system without the proposed PIDf + F(s) control scheme, the AVR with the proposed technique, and the AVR with the PIDf + F(s) with the introduction of disturbance in form of load variation at 25 seconds. The performance of the proposed scheme was compared with conventional PID control AVR system without F(s). The results of the comparison indicated that the proposed technique provided superior performance in terms of percentage overshoot and settling time. Generally, the PIDf with F(s) control scheme was more stable than the conventional PID controller as indicated by the percentage overshoot, which was 4.58% for PIDf and 4.28% for PIDf + F(s).

Automatic Voltage Regulator Control System for Synchronous Generator

Presented in this paper is Automatic Voltage Regulator (AVR) Control System for Synchronous Generator. A nonlinear model of synchronous generator was developed in Simulink and was later linearized as a single input single output (SISO) transfer function model. A Proportional Integral and Derivative (PID) controller was designed and by using the MATLAB/Simulink PID tuner, the gains of the proportional, integral and derivative parameters were obtained. A Low Pass Filter, F(s) was designed and introduced as part of the input signal to eliminate any noise effect that may be introduced into the AVR control system through the input. Simulations were carried out considering basically three scenarios viz: the AVR control system without the proposed PIDf + F(s) control scheme, the AVR with the proposed technique, and the AVR with the PIDf + F(s) with the introduction of disturbance in form of load variation at 25 seconds. The performance of the proposed scheme was compared with conventional PID control AVR system without F(s). The results of the comparison indicated that the proposed technique provided superior performance in terms of percentage overshoot and settling time. Generally, the PIDf with F(s) control scheme was more stable than the conventional PID controller as indicated by the percentage overshoot, which was 4.58% for PIDf and 4.28% for PIDf + F(s).

An adaptive fractional controller design for automatic voltage regulator system: sigmoid-based fractional-order PID controller

The primary objective of a power system is to provide safe and reliable electrical energy to consumers. This objective is achieved by maintaining the stability of the power system, a multifaceted concept that can be divided into three distinct classes. The focus of this study is on one of these classes, voltage stability. A critical component in maintaining voltage stability is the automatic voltage regulator (AVR) system of synchronous generators. In this paper, a novel control method, the sigmoid-based fractional-order PID (SFOPID), is introduced with the aim of improving the dynamic response and the robustness of the AVR system. The dandelion optimizer (DO), a successful optimization algorithm, is used to optimize the parameters of the proposed SFOPID control strategy. The optimization process for the DO-SFOPID control strategy includes a variety of objective functions, including error-based metrics such as integral of absolute error, integral of squared error, integral of time absolute error, and integral of time squared error, in addition to the user-defined Zwee Lee Gaing’s metric. The effectiveness of the DO-SFOPID control technique on the AVR system has been rigorously investigated through a series of tests and analyses, including aspects such as time domain, robustness, frequency domain, and evaluation of nonlinearity effects. The simulation results are compared between the proposed DO-SFOPID control technique and the fractional-order PID (FOPID) and sigmoid-based PID (SPID) control techniques, both of which have been tuned using different metaheuristic algorithms that have gained significant recognition in recent years. As a result of these comparative analyses, the superiority of the DO-SFOPID control technique is confirmed as it shows an improved performance with respect to the other control techniques. Furthermore, the performance of the proposed DO-SFOPID control technique is validated within an experimental setup for the AVR system. The simulation results show that the proposed DO-SFOPID control technique is highly successful in terms of stability and robustness. In summary, this study provides comprehensive evidence supporting the effectiveness and superiority of the DO-SFOPID control technique on the AVR system through both simulation and experimental results.

A New Hybrid Intelligent Fractional Order Proportional Double Derivative + Integral (FOPDD+I) Controller with ANFIS Simulated on Automatic Voltage Regulator System

In the dynamic realm of Automatic Voltage Regulation (AVR), the pursuit of robust transient response, adaptability, and stability drives researchers to explore novel avenues. This study introduces a groundbreaking approach—the Hybrid Intelligent Fractional Order Proportional Derivative2+Integral (FOPDD+I) controller—leveraging the power of the Adaptive Neuro-Fuzzy Inference System (ANFIS). The novelty lies in the comparative analysis of three scenarios: the AVR system without a controller, with a traditional PID controller, and with the proposed FOPDD+I-based ANFIS. By fusing ANFIS with a hybrid controller, we forge a unique path toward optimized AVR performance. The hybrid controller, based on FOPID (Fractional Order Proportional Integral Derivative) principles, synergizes individual integral factors with ANFIS, augmenting them with a doubled derivative factor. The ANFIS design employs a hybrid optimization learning scheme to fine-tune the Fuzzy Inference System (FIS) parameters governing the AVR system. To train the fuzzy inference system, we utilize a Proportional-Integral-Derivative (PID) simulation of the entire AVR system, capturing essential data over approximately seven seconds. Our simulations, conducted in MATLAB/Simulink, reveal impressive performance metrics for the FOPDD+I-ANFIS approach: Rise time: 1.1162 seconds, settling time: 0.5531 seconds, Overshoot: 0%, Steady-state error: 0.00272, These results position our novel approach favorably against existing works, underscoring the transformative potential of intelligent creation in AVR control.

Fractional High-Order Differential Feedback Controller Based on Meta-Heuristic Algorithms for Automatic Voltage Regulator

In this study, optimally parameter tuning of fractional high-order differential feedback controller (FHODFC) for an automatic voltage regulator (AVR) system regulating the terminal voltage of the synchronous generator is presented. Recently developed meta-heuristic optimization techniques named honey badger algorithm (HBA) and gorilla troops optimizer (GTO) have been employed for the first time in order to optimally determine ten different parameters of the FHODFC. The performance of the proposed controllers, that is, HBA-FHODFC and GTO-FHODFC, is analyzed in terms of transient and steady state responses, and robustness against uncertainties and external disturbances. In addition, their performance is compared to a FHODFC of which parameters were tuned by using particle swarm optimization algorithm in a previous study. The results indicate that the proposed HBA-FHODFC and GTO-FHODFC controllers provide zero overshoot and better results in terms of settling time and objective function then the PSO tuned FHODFC. Therefore, the proposed controllers can be used as an effective method in the AVR system.

Designing Heuristic-Based Tuners for Fractional-Order PID Controllers in Automatic Voltage Regulator Systems Using a Hyper-Heuristic Approach

This work introduces an alternative approach for developing a customized Metaheuristic (MH) tailored for tuning a Fractional-Order Proportional-Integral-Derivative (FOPID) controller within an Automatic Voltage Regulator (AVR) system. Leveraging an Automated Algorithm Design (AAD) methodology, our strategy generates MHs by utilizing a population-based Search Operator (SO) domain, thus minimizing human-induced bias. This approach eliminates the need for manual coding or the daunting task of selecting an optimal algorithm from a vast collection of the current literature. The devised MH consists of two distinct SOs: a dynamic swarm perturbator succeeded by a Metropolis-type selector and a genetic crossover perturbator, followed by another Metropolis-type selector. This MH fine-tunes the FOPID controller’s parameters, aiming to enhance control performance by reducing overshoot, rise time, and settling time. Our research includes a comparative analysis with similar studies, revealing that our tailored MH significantly improves the FOPID controller’s speed by 1.69 times while virtually eliminating overshoot. Plus, we assess the tuned FOPID controller’s resilience against internal disturbances within AVR subsystems. The study also explores two facets of control performance: the impact of fractional orders on conventional PID controller efficiency and the delineating of a confidence region for stable and satisfactory AVR operation. This work’s main contributions are introducing an innovative method for deriving efficient MHs in electrical engineering and control systems and demonstrating the substantial benefits of precise controller tuning, as evidenced by the superior performance of our customized MH compared to existing solutions.

Artificial rabbits optimization algorithm based tuning of PID controller parameters for improving voltage profile in AVR system using IoT

The power system is mainly affected by transient situations caused by switching heavy loads. The system may become unstable when transient situations occur. The power system should be able to perform continuous operation to maintain its voltage within acceptable limits. To achieve more stability and increase its speed of response, an Automatic Voltage Regulator (AVR) system requires the inclusion of a controller. The AVR system in the generating station uses the PID controller to adjust the abnormal voltage caused by transient conditions. To maintain the nominal voltage level under all the load conditions in the system, a bio-inspired meta-heuristic algorithm called Artificial Rabbit Optimization (ARO) algorithm is proposed to tune the PID controller gain parameters and obtain the optimal gain, thereby the AVR system adjusts the generator terminal voltage to nominal levels. The ARO algorithm inspires natural survival techniques to improve the AVR performance by reducing errors. To maintain a stable voltage profile in a power system, this research mathematically models survival techniques using the Internet of Things (IoT) to obtain an optimal solution. As a result, all devices connected to the power network receive a stable voltage that ensures their voltage reliability. The effectiveness of the proposed algorithm for the AVR system is verified with the MATLAB R2022a model, and the statistics functions are implemented in the module of Pandas, Scipy and mathematical investigations done in Numpy. The proposed ARO algorithm achieves a better voltage profile with less than 12.63% maximum peak overshoot during the system's transient response. The proposed algorithm provides the fastest response and highest stability comparable to other optimisation algorithms.

On the assessment of meta-heuristic algorithms for automatic voltage regulator system controller design: a standardization process

On the assessment of meta-heuristic algorithms for automatic voltage regulator system controller design: a standardization process

Load frequency and voltage control of two area interconnected power system using covariance matrix adaptation evolution strategy based Aquila optimization optimized fast fuzzy-fractional order tilt integral derivative controller

In this paper, an optimal tuning method is given for a Fast Fuzzy-Fractional Order Tilt Integral Derivative Controller (HFF-FOTIDC) used in the Load Frequency Control (LFC) and Automatic Voltage Regulator (AVR) systems of a two-area interconnected power system. LFC is in charge of managing the frequency and real power flows in the system. AVR is in charge of maintaining the voltage profile and managing the flow of reactive power. In Area 1, a step disturbance is used to study how the system works in a dynamic way. By looking at the system's voltage, power flow through the tie lines, and frequency, the experts can figure out how the system will react. The proposed control techniques combine two well established control logic, which are fast fuzzy control logic and fractional order tilt integral derivative control logic. The main goal is to decrease voltage and frequency changes caused by step disturbances and return them to their normal values. The simulation results show how good the suggested system is by comparing it to systems that use traditional proportional integral controllers and traditional Integral controllers. By using the finest tuning method for the fast fuzzy-fractional order tilt integral derivative controller, the designed system has better performance than standard controllers at reducing fluctuations and getting the desired frequency and voltage responses. The parameters of the proposed controller are optimized by using Covariance Matrix Adaptation Evolution Strategy based Aquila Optimization (CMAESAO) optimization technique.

Development of AVR controller performance using exponential distribution and transit search optimization techniques

This paper attempts to obtain the optimal solution to enhance the performance of the Automatic Voltage Regulator (AVR) Controller, as it is an essential tool to control the synchronous generator output voltage. The controller improves AVR system stability and response time; moreover, it is demonstrated that the Proportional Integral Derivative (PID) controller achieves the goal by applying two artificial intelligence techniques to design the optimal values of the Automatic Voltage Regulator (AVR) PID controller for a single area model. The first is the Exponential Distribution Optimization Algorithm (EDO), and the second is the Transit Search Optimization Algorithm (TS). EDO and TS are used to determine the best PID controller parameters and have also recently been developed in the breadth of optimization problems and associated computational complexities field. The objective function, Integrated Square Error (ISE), minimizes the error voltage for improved stability and response. The outcomes are compared to various optimization techniques to prove the validation of the two proposed methods. The results show that the EDO and TS proved their superiority through their stability level to the AVR system and their steady-state error improvement. Moreover, the dominant effect of damping frequency decreases the oscillation and the reduced maximum overshoot that protects the loads from being subjected to non-permissible over-voltage levels. Finally, a robustness test is applied to the two proposed optimization methods to prove their reliability and effectiveness.

Jellyfish Search Optimized Gated Recurrent Unit Based FOPID/PID for Enhanced Automatic Voltage Regulator

The most essential mechanism for regulation in power systems is the Automatic Voltage Regulator (AVR) device, which maintains generator terminal voltage within predefined parameters. This research paper introduces an innovative approach to enhance AVR system performance through the ideal setting for a fractional-order PID (FOPID) controller that leverages Bidirectional Gate Recurrent Unit (BiGRU)-based deep learning techniques in conjunction with the Jellyfish Search Optimization (JSO) algorithm. To enhance flexibility and precision in control, the conventional proportional integral derivative (PID) controller is replaced with a FOPID controller, whose parameters are optimized by means of the JSO algorithm. The effectiveness of the proposed Jellyfish Search Optimization-based most advanced controllers (JSO-FOPID/PID) is compared with the existing FOPID controller implemented using the Seagull Optimization Algorithm (SOA-FOPID). Comprehensive analyses, including transient time, robustness, stability, and convergence analysis, are conducted subject to applying both stable and alternating load situations to validate the proposed approach. Experimental validation is performed using the MATLAB R2022a application with FOMCON tools and a laboratory prototype for testing. The simulations and experiments demonstrate that the projected AVR consistently delivers optimum dynamic responses and enhances overall stability when compared to existing methods, underscoring its efficacy in voltage regulation and power system control.

Robust control for automatic voltage regulator system based on learning sliding mode control

Robust control for automatic voltage regulator system based on learning sliding mode control

A Novel Salp Swarm Optimization Oriented 3-DOF-PIDA Controller Design for Automatic Voltage Regulator System

A Novel Salp Swarm Optimization Oriented 3-DOF-PIDA Controller Design for Automatic Voltage Regulator System

Robust control for automatic voltage regulator system based on learning sliding mode control

Robust control for automatic voltage regulator system based on learning sliding mode control