Automatic Voltage Regulator System Research Articles

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.

Comment on “Optimal tuning of sigmoid PID controller using nonlinear sine cosine algorithm for the automatic voltage regulator system”

In this short note, the relative stability results (based on root locus and Bode analyses) reported in “Optimal tuning of sigmoid PID controller using nonlinear sine cosine algorithm for the automatic voltage regulator system” (Suid and Ahmad, 2022) are analyzed. It is shown that the use of closed-loop transfer functions (CLTFs) should be avoided in relative stability analysis since they may lead to less accurate stability results. The present study affirms that the results of relative stability analysis obtained using loop transfer functions (LTFs) are more reliable and accurate.

Design of PIDDα Controller for Robust Performance of Process Plants

Managing industrial processes in real-time is challenging due to the nonlinearity and sensitivity of these processes. This unpredictability can cause delays in the regulation of these processes. The PID controller family is commonly used in these situations, but their performance is inadequate in systems and surroundings with varying set-points, longer dead times, external noises, and disturbances. Therefore, this research has developed a novel controller structure for PIDDα that incorporates the second derivative term from PIDD2 while exclusively using fractional order parameters for the second derivative term. The controllers’ robust performance has been evaluated on four simulation plants: first order, second order with time delay, third-order magnetic levitation systems, and fourth-order automatic voltage regulation systems. The controllers’ performance has also been evaluated on experimental models of pressure and flow processes. The proposed controller exhibits the least overshoot among all the systems tested. The overshoot for the first-order systems is 9.63%, for the third-order magnetic levitation system, it is 12.82%, and for the fourth-order automatic voltage regulation system, it is only 0.19%. In the pressure process plant, the overshoot is only 4.83%. All controllers for the second-order systems have a time delay, while the flow process plant has no overshoot. The proposed controller demonstrates superior settling times in various systems. For first-order systems, the settling time is 14.26 s, while in the pressure process plant, the settling time is 8.9543 s. Similarly, the proposed controllers for the second-order system with a time delay and the flow process plant have the same settling time of 46.0495 s. In addition, the proposed controller results in the lowest rise time for three different systems. The rise time is only 0.0075 s for the third-order magnetic levitation system, while the fourth-order automatic voltage regulation system has a rise time of 0.0232 s. Finally, for the flow process plant, the proposed controller has the least rise time of 25.7819 s. Thus, in all the cases, the proposed controller results in a more robust controller structure that provides the desired performance of a regular PIDD2 controller, offering better dynamic responses, shorter settling times, faster rise times, and reduced overshoot. Based on the analysis, it is evident that PIDDα outperforms both PID and FOPID control techniques due to its ability to produce a more robust control signal.

Optimal tuning of the novel voltage regulation controller considering the real model of the automatic voltage regulation system

This paper deals with the optimal tuning of the controller for the real automatic voltage regulation (AVR) system of the synchronous generator (SG). For this purpose, firstly, a novel proportional-integral controller with two degrees of freedom and anti-windup protection for application in the AVR system is proposed. Secondly, in order to determine the optimal parameters of such a controller, the objective function which takes into account transient response characteristics, disturbance, and measurement noise rejection capabilities of the AVR system is presented. Furthermore, the adaptive modification of the existing metaheuristic African vultures optimization algorithm (AVOA) is introduced for controller parameters design. Finally, unlike the many existing papers in the available literature which use a simplified model of the AVR system, in this work the simulation model of the AVR system is realized by observing the technical documentation of the excitation system of the 40 MVA SG from a hydropower plant Perucica in Montenegro. The results obtained in this work have proven that the proposed AVR controller has superior performances compared with other frequently used controllers in real power plants, in terms of providing transient response quality of the SG terminal voltage, disturbance rejection, and measurement noise mitigation abilities. Additionally, increased convergence speed and improved criterion function value demonstrated that the proposed adaptive modification of the AVOA algorithm outperforms some of the most popular metaheuristic algorithms.© 2017 Elsevier Inc. All rights reserved.

An Improved Marine Predators Algorithm-Tuned Fractional-Order PID Controller for Automatic Voltage Regulator System

One of the most popular controllers for the automatic voltage regulator (AVR) in maintaining the voltage level of a synchronous generator is the fractional-order proportional–integral-derivative (FOPID) controller. Unfortunately, tuning the FOPID controller is challenging since there are five gains compared to the three gains of a conventional proportional–integral–derivative (PID) controller. Therefore, this research work presents a variant of the marine predators algorithm (MPA) for tuning the FOPID controller of the AVR system. Here, two modifications are applied to the existing MPA: the hybridization between MPA and the safe experimentation dynamics algorithm (SEDA) in the updating mechanism to solve the local optima issue, and the introduction of a tunable step size adaptive coefficient (CF) to improve the searching capability. The effectiveness of the proposed method in tuning the FOPID controller of the AVR system was assessed in terms of the convergence curve of the objective function, the statistical analysis of the objective function, Wilcoxon’s rank test, the step response analysis, stability analyses, and robustness analyses where the AVR system was subjected to noise, disturbance, and parameter uncertainties. We have shown that our proposed controller has improved the AVR system’s transient response and also produced about two times better results for objective function compared with other recent metaheuristic optimization-tuned FOPID controllers.

Output performance evaluation of the automatic voltage regulator system on pre-filter control technique

<p>Automatic voltage regulator (AVR) is used at each generating station or synchronous generators to maintain voltage supply at steady state or at a constant value otherwise the performance of the generator will be affected. The combination of AVR system with controllers clears fluctuation due to variation in load, speed, temperature, and power factor. This causes deviation in generator’s voltage and damage to power equipment. This work presents performance enhancement of AVR system using Proportional Integral and Derivative (PID) with pre-filter control technique. The enhanced performance was achieved by designing a nonlinear model of a synchronous generator, a PID controller and a low pass filter or pre-filter using MATLAB/Simulink model. The introduction of the proposed PID controller with LPF at load variation of 20 seconds, reduces the rise time and the peak time to 5.2975 seconds and 12.31 seconds respectively. This increases the overshoot and the settling time to 4.28 seconds and 17.60 seconds. However, the developed scheme provides a stable time response performance for various desired generator voltages considered. The performance of the proposed scheme was compared with conventional PID controlled AVR system without LPF. The proposed scheme provides more stability as indicated by the percentage overshoot, which is 4.5788% (for PID) and 4.2765% (for PID with prefilter). This has contributed to knowledge an AVR control system with better performance in terms of rise time, peak time, overshoot and settling time for stabilized generator output voltage.</p>

On the control interaction of synchronous machine and inverter-based resources during system-split situations

Motivated by the potential transient oscillation risks in inverter based resource (IBR) dominated power systems, the paper is to identify the unwanted control interactions between traditional synchronous machines (SMs) and IBRs during system-split situations. Modal impedance analysis method is applied to reveal oscillation risk and the underlying mechanism, while electromagnetic transient (EMT) simulation analysis is used to observe reliable oscillation phenomenon. Impedance models of IBRs considering both grid-following (GFL) and grid-forming (GFM) control schemes and SMs equipped with automatic voltage regulator (AVR) and power system stabilizer (PSS) are developed for grid conditions with large frequency and/or voltage deviations, and validated by EMT simulations. Potential adverse control interactions within an exemplary test system are analyzed taking into account the impacts of: 1) renewable penetration level; 2) converter control scheme (GFL or GFM); 3) converter control tuning; 4) SM control setting; 5) grid-support capabilities of converter-interfaced loads. It has been observed that oscillation instability at around several Hz can occur by high penetration of GFL controlled IBRs after system-splits, which can be mitigated by redesigning the AVR & PSS control, or retuning the power/voltage control loops and phase-locked loop (PLL) of GFL converter. However, such measures might inevitably induce other stability issues, e.g. synchronization instability. From the oscillation analysis, it has also been revealed, through replacing part of the GFL converters with GFM converter(s), the oscillation mode at several Hz can disappear, owning to the varied impedance including better damping of GFM than GFL for the frequencies between 1 and 10 Hz.

Metaheuristic-Assisted Advanced Control Approach for Time Delayed Unstable Automatic Voltage Regulator

This work proposes a two-degree-of-freedom factional order proportional-integral-derivative (2-DOF FOPID) controller for automatic voltage regulator (AVR) to mitigate the impact of voltage transients and set-point variations in any power system network. An inherent communication time delay is considered in the investigated AVR plant. This inherent time delay occurs due to the presence of amplifier, exciter, generator, and sensor, making the system unstable. Such an unstable AVR system is still unexplored and difficult to control. The suggested control structure of the 2-DOF FOPID is flexible enough from the perspective of tuning and provides enhanced performance of an AVR system at the cost of a large number of controller design parameters. Therefore, the proposed controller is optimized by an advanced meta-heuristic technique; squirrel search algorithm (SSA) which leads to SSA tuned 2-DOF fractional order PID (S2DFPID) controller. Moreover, selecting an appropriate meta-heuristic approach is a very tedious task for such unstable systems and advanced control architecture. Hence, a systematic statistical approach is also employed to select an optimization technique for the specified purpose, and a rigorous comparative assessment of seven state-of-the-art optimization techniques is also presented in this work. Investigation reveals the superior performance of S2DFPID controller compared to SSA-tuned fractional order PID (SFPID), and SSA-tuned traditional PID (SPID) controllers in the presence of set-point variations, external load disturbances and parametric uncertainties.

Load bank system modification for appropriatediesel generator testing by programmable resistive load

The paper presents adaptation of a commercial AC load bank system modified to deliver controlled load to the diesel generators in order to prevent generator wet-stacking. The described solution involves increasing the existing load resolution from 6*53 kW to a resolution of 56 steps with 5.7 kW, making an approximate total load of 319 kW. The load can be controlled in steps of ±5.7 kW, ±17 kW, and ±51 kW. Measurement of electrical quantities was realized with PM51100 power meter. For the purpose of recording electrical quantities, a low-price solution with a RecMod2021 device using Modbus communication protocol was developed. The RecMod2021 device is programmed to read the PM51100 registers and log read quantities to the belonging SD card. The format of the recorded electrical quantities was adapted for later graphical representation and analysis of the power supply quality, as well as the stability of the generator automatic voltage regulation (AVR) system under various load scenarios.

A self-competitive mutation strategy for Differential Evolution algorithms with applications to Proportional–Integral–Derivative controllers and Automatic Voltage Regulator systems

The Differential Evolution (DE) algorithm is a powerful and simple optimizer for solving various optimization problems. Based on the literature, DE has shown suitable performance in exploring search spaces and locating global optimums. However, it is typically slow in extracting the problem solution. In this paper, the exploration ability of the DE algorithm is augmented with a competitive control parameter ω based on the value of the objective function of the mutating members. A new mutation strategy is introduced, subtracting weaker members from superior weaker ones. The proposed DE algorithm, which is referred to as the self-competitive DE, has been employed for solving real-world optimization problems. Several DE algorithms are enhanced with the proposed parameter ω, and the efficiencies of the resulting enhanced algorithms are tested. Furthermore, the optimal Proportional–Integral–Derivative (PID) controller tuning for an Automatic Voltage Regulator (AVR) system is used to investigate the effectiveness of the proposed strategy in solving real-world optimization problems. Simulation results demonstrate a good performance of the proposed parameter ω over several other well-known DE algorithms.

Enhancing transient response performance of automatic voltage regulator system by using a novel control design strategy

Enhancing transient response performance of automatic voltage regulator system by using a novel control design strategy

A reinforcement learning approach to Automatic Voltage Regulator system

An Automatic Voltage Regulator (AVR) system utilized to keep the terminal voltage of a synchronous generator at the desired level has received much attention among researchers. Designing an efficient and robust control scheme for the AVR system to maintain a specified voltage level is an important research area. From the control area perspective, reinforcement learning, an adaptive optimal control method, has received increasing attention in reference tracking problems. This article discusses a reinforcement learning approach to an AVR system and its experimental validation. A deep deterministic policy gradient (DDPG) agent working in continuous-time is designed offline to improve dynamic system characteristics of the AVR system besides its robustness against load disturbance, parameter uncertainties, and reference change. In the DDPG agent design process, the limits of the produced control signal are taken into account to perform a feasible simulation similar to a real-time application. The performance of the proposed learning-based controller is analyzed in three categories: transient and steady-state responses, stability analysis, and robustness analysis against parameter uncertainties, reference change, and load disturbance. A comparison with recently published papers employing Fuzzy-PID, PID-F, PIλDND2N2, PIDD2 ,and PID controllers in which various heuristic optimization algorithms were employed to optimally tune the controller parameters is made. Furthermore, to demonstrate that the behavior of the learning-based approach provides a stable and satisfactory performance, it is analyzed for a real synchronous generator connected to a 230 kV network using Matlab/Simulink environment. The results presented in this paper indicate that the proposed learning-based controller ensures the stability of the AVR system, significantly improves the regulating performance, and most impressively, is robust against parameter uncertainties, reference change, and load disturbance.