Automatic Voltage Regulator System Research Articles

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

AbstractThe 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.

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

Optimizing Automatic Voltage Regulation: A Deep Reinforcement Learning Approach

This paper presents a novel approach to enhance the performance of Automatic Voltage Regulator (AVR) systems in power systems using Deep Reinforcement Learning (DRL). The AVR plays a critical role in maintaining voltage stability and ensuring reliable power delivery. However, conventional control strategies, such as PID controllers, have limitations in handling complex and nonlinear power system dynamics. In this study, the application of DRL techniques is explored, particularly the Twin-Delayed Deep Deterministic Policy Gradient (TD3) algorithm, to AVR control. This algorithm offers the advantage of handling continuous action spaces and enable the controller to learn optimal control policies directly from the system's state information. The results show that the DRL approach outperforms the traditional PID and Neural Network-based control approaches, with the shortest time response and the best voltage regulation performance. The use of DRL in AVR system control shows promising potential for improving the efficiency and accuracy of power system control. This research provides insights into the advantages of DRL for process control and highlights its potential for future applications in power system control.

Performance analysis of voltage profile improvement in AVR system using zebra optimization algorithms based on PID controller

Many problems are associated with dynamic changes in the power system, mainly because the number of switching loads has increased. These loads induce ineffective system behaviour, reduce the system voltage profile, and lead to improper voltage regulation. The proposed Automatic Voltage Regulator (AVR) system is intended to eliminate the above problem and preserve the system voltage at the generator terminal within the allowable range. In this work, Proportional-Integral-Derivative (PID) controllers are applied to enhance the dynamic behaviour of the AVR system. The Zebra Optimization Algorithm (ZOA) and Osprey Optimization Algorithm (OOA) algorithms are implemented to optimize the system's transient response by modifying the gain of PID controller parameters, which include proportional, integral, and derivative parameters, to preserve a stable voltage profile. To achieve an optimal solution, the mathematical model of the proposed system is realized in the Internet of Things (IoT). The connected loads in the system can operate at the nominal voltage level through PID controller tuning using ZOA and OOA. The proposed system's performance is evaluated and verified with MATLAB R2023a. ZOA showed an improvement of 18.5 % with a peak overshoot of 0.01 % during the system-transient condition. Compared to conventional optimization algorithms implemented in AVR systems, ZOA provides faster responsiveness and enhanced system stability.

A Novel Balanced Arithmetic Optimization Algorithm-Optimized Controller for Enhanced Voltage Regulation

This work introduces an innovative approach that unites a PIDND2N2 controller and the balanced arithmetic optimization algorithm (b-AOA) to enhance the stability of an automatic voltage regulator (AVR) system. The PIDND2N2 controller, tailored for precision, stability, and responsiveness, mitigates the limitations of conventional methods. The b-AOA optimizer is obtained through the integration of pattern search and elite opposition-based learning strategies into the arithmetic optimization algorithm. This integration optimizes the controller parameters and the AVR system’s response, harmonizing exploration and exploitation. Extensive assessments, including evaluations on 23 classical benchmark functions, demonstrate the efficacy of the b-AOA. It consistently achieves accurate solutions, exhibits robustness in addressing a wide range of optimization problems, and stands out as a promising choice for various applications. In terms of the AVR system, comparative analyses highlight the superiority of the proposed approach in transient response characteristics, with the shortest rise and settling times and zero overshoot. Additionally, the b-AOA approach excels in frequency response, ensuring robust stability and a broader bandwidth. Furthermore, the proposed approach is compared with various state-of-the-art control methods for the AVR system, showcasing an impressive performance. These results underscore the significance of this work, setting a new benchmark for AVR control by advancing stability, responsiveness, and reliability in power systems.

Design of FOPID Controller for Optimizing AVR System using Harris Hawk Algorithm

The Automatic Voltage Regulator (AVR) system takes care of maintaining the terminal voltage of the generation unit within safe limitations. But the performance is irregular and delayed. A controller is used in conjunction with the AVR system to improve this. In Automatic Voltage Regulator the FOPID Controller is the main component. FOPID beats the other controllers in terms of its control capabilities and tuning adaptability. The basic role of this examination is to decide how best to utilize this idea while making automatic voltage regulators (AVR), But the Tuning is more complicated due to FOPID’s additional parameters (λ and μ). To overcome complexity in this research, the Harris Hawk optimization (HHO) technique is based on a bionic intelligent optimum tuning approach. Gains in FOPID efficiency may be maximized by eliminating absolute error in integral time (IATE), or Fitness Function. MATLAB/SIMULINK is used to double-check the findings, which the findings of a comparison between the suggested HHO-based optimum AVR design and a previously published GBO-based optimal design of AVR demonstrate that the former delivers the superior dynamic response and increased stability.

Design of FOPID Controller for Optimizing AVR System using Harris Hawk Algorithm

The Automatic voltage regulator (AVR) system maintains the terminal voltage of the generation unit within safe limitations. But the performance is irregular and delayed. A controller is used in conjunction with the AVR system to improve this. In AVR, the FOPID Controller is the main component. FOPID beats the other controllers in terms of its control capabilities and tuning adaptability. The basic role of this examination is to decide how best to utilize this idea while making AVR, But the Tuning is more complicated due to FOPID's additional parameters (λ and μ). To overcome complexity in this research, the Harris Hawk optimization (HHO) technique is based on a bionic intelligent optimum tuning approach. Gains in FOPID efficiency may be maximized by eliminating absolute error in integral time (IATE), or fitness function. MATLAB/SIMULINK is used to double-check the findings, which the findings of a comparison between the suggested HHO-based optimum AVR design and a previously published GBO-based optimal design of AVR demonstrate that the former delivers the superior dynamic response and increased stability.

Black Widow Optimization for Power System Load Frequency Control: A Comparative Study

This research paper mainly engrossed on developing a suitable novel tuning methodology named Black Widow Optimization (BWO) Algorithm for power system optimization problems. Load Frequency Control (LFC) and Automatic Voltage Regulator (AVR), two of the most important control systems in the power system arena, are employed as test systems to assess the efficiency of the suggested BWO approach. Various analyses, such as transient analysis, are employed to evaluate the efficiency of the suggested BWO approach in LFC and AVR systems. robustness analysis and convergence analysis. The significant performance measures of test system such as output power of each generating unit, maximum peak, settling time, steady state error, rise time and voltage/frequency deviations of the frequency and voltage responses are considered for the comparison purposes. The comparative analysis clearly demonstrates that the proposed novel tuning methodology provides better transient performances subjected to Step Load Perturbations (SLP), Variable Load Perturbations (VLP), and robustness performances under a wide range of load and parameter changes ranging from -50% to +50%. The convergence analysis of BWO algorithm also performed in LFC and AVR test systems and results better convergence characteristics with minimal convergence time as well as least fitness value is obtained with the BWO algorithm.

Controller design for automatic voltage regulator system using modified opposition-based weighted mean of vectors algorithm

ABSTRACT This paper proposes a modified optimization technique to determine the parameters of a real proportional-integral-derivative plus second-order derivative (real PIDD2) controller adopted in an automatic voltage regulator (AVR). In this regard, a modified opposition learning (mOBL) based weighted mean of vectors (INFO) algorithm (mOBL-INFO) is proposed for the first time. The performance of the proposed algorithm was initially tested on several benchmark functions with unimodal, multimodal, and low-dimensional properties. The obtained results against test functions were compared with the original INFO algorithm as the latter has already been shown to present superior results against several recent and effective metaheuristic algorithms. The developed mOBL-INFO algorithm was then used to adjust a real PIDD2 controller for the AVR system. The performance of the proposed method was tested using several analyses such as transient response, stability, and robustness. The related analyses demonstrated good promise of the proposed method for AVR system control. Furthermore, previously reported 26 effective methods were also employed to assess the performance of the mOBL-INFO-based real PIDD2 controller for the AVR system. The obtained results demonstrated that the proposed method in this study has an excellent transient response performance for AVR system control.

Controller Design for Automatic Voltage Regulator System with Fitness Distance Balance Based Artificial Ecosystem Optimization Algorithm

Güç sistemlerinde kullanılan cihazlar nominal gerilimlerinde çalışmak üzere tasarlanmıştır. Senkron generatörlerin terminal gerilimlerinde yaşanan değişimler bu cihazların ömürlerinin kısalmasına neden olabilir. Bu değişimleri ortadan kaldırmak için senkron generatörler uyartım akımlarını düzenleyen Otomatik Gerilim Regülatörü (OGR) sistemleri ile donatılmaktadır. OGR sistemlerinde kullanılan denetleyicilerin tasarımı sistem performansı için önemli bir konudur. Bu çalışmada Uygunluk Mesefe Dengelesi (FDB) yöntemi ile geliştirilmiş Yapay Ekosistem Tabanlı Optimizasyon (AEO) algoritması kullanılarak PID-OGR ve FOPID-OGR sistemlerinin tasarımı yapılmıştır. Tasarımı yapılan sistemler adım yanıtı ve frekans yanıtı açısından literatürde daha önce yapılmış olan benzer çalışmalarla karşılaştırılmıştır. Analiz sonuçları FDB-AEO algoritması ile tasarlanan PID ve FOPID denetleyicili OGR sistemlerinin performanslarındaki üstünlüğü göstermiştir.

A fractional order PID tuning tool for automatic voltage regulator using marine predators algorithm

The fractional-order proportional-integral-derivative (FOPID) controller stands as a widely embraced choice for the task of automatic voltage regulation (AVR) when it comes to maintaining the voltage output of synchronous generators. Nevertheless, fine-tuning the FOPID controller presents a formidable challenge, mainly because it possesses five tuning gains, in contrast to the conventional PID controller, which has three gains. Consequently, this paper introduces a novel tuning tool tailored to the AVR system by utilizing the marine predators algorithm (MPA). To gauge the effectiveness of the proposed approach, two key evaluation criteria are employed: step response analysis and trajectory tracking analysis. The results of this research reveal that the MPA-FOPID controller demonstrates exceptional performance criteria, notably enhancing the AVR transient response in comparison to other FOPID controllers optimized through recent metaheuristic algorithms.