Design and performance analysis of PID controller for an AVR system using multi-objective non-dominated shorting genetic algorithm-II

An improved RUN optimizer based real PID plus second-order derivative controller design as a novel method to enhance transient response and robustness of an automatic voltage regulator

Optimal tuning of proportional-integral-derivative (PID) controller parameters is necessary for the satisfactory operation of automatic voltage regulator (AVR) system. This study presents a tuning fuzzy logic approach to determine the optimal PID controller parameters in AVR system. The developed fuzzy system can give the PID parameters on-line for different operating conditions. The suitability of the proposed approach for PID controller tuning has been demonstrated through computer simulations in an AVR system.

A novel performance criterion approach to optimum design of PID controller using cuckoo search algorithm for AVR system

Automatic voltage regulator (AVR) systems are critically required in many systems such as hydro, gas, steam turbines and engines. Effective control of an AVR system is complex and challenging due to its unstable open loop response characteristics. The AVR system is required to sustain appropriate voltage levels in spite of fluctuations in the main supply. Hence, the AVR system needs to be primarily designed for stability. This paper presents design implementation of the first and second generations of CRONE controllers (Commande Robuste d'Ordre Non Entier) for an AVR system. CRONE is a non-integer order robust controller which is far more effective than the standard PID controllers as it offers better tuning and flexibility in controlling the AVR process. In the present study, various time-domain performance criteria with and without controller were simulated to validate the accomplishment of AVR CRONE controllers. The proposed control procedure for enhancing AVR systems' efficiency has been shown to be effective and better in terms of improved phase and gain margins.

In this paper, various standard performance indices are minimized for Proportional Integral Derivative (PID) controller based Automatic Voltage Regulator(AVR) system using Harris Hawks Algorithm (HHO). This paper also proposes the best performance index for HHO-PID based AVR system. Efficient minimization of performance indices yields better AVR system controller parameters. Using obtained controller parameters, we found step responses and compared values of \(t_{r} ,t_{s} ,M_{p}\) and \(E_{ss}\) and then proposed the best efficient minimized performance index. HHO-PID based AVR system is also compared with GA-PID based AVR system for proving superiority of HHO algorithm. HHO-PID based AVR system with proposed best performance index showed great improvement in step response.

This paper presents a method for robust optimum tuning of Fractional-order Proportional Integral Derivative (FoPID) controller for Automatic Voltage Regulator (AVR) system. Optimal tuning of FoPID parameters for AVR system are accomplished by using Salp Swarm Algorithm (SSA) of optimization. The performance of the AVR system with FoPID controller tuned by SSA shows the robust performance against disturbances deliberated at the output of the system. To test the robustness of the system, in the presence of external disturbance $H_{\infty}$ norm is considered. AVR system with SSA tuned PID controller is also simulated with MATLAB platform. Results shows that the AVR system with SSA tuned FoPID controller outperform the SSA tuned PID controller in the presence of external disturbances at output of the system and shows its disturbance rejection capabilities.

Introduction. In an electrical power system, the output of the synchronous generators varies due to disturbances or sudden load changes. These variations in output severely affect power system stability and power quality. The synchronous generator is equipped with an automatic voltage regulator to maintain its terminal voltage at rated voltage. Several control techniques utilized to improve the response of the automatic voltage regulator system, however, proportional integral derivative (PID) controller is the most frequently used controller but its parameters require optimization. Novelty. In this paper, the chaotic sequence based on the logistic map is hybridized with particle swarm optimization to find the optimal parameters of the PID for the automatic voltage regulator system. The logistic map chaotic sequence-based initialization and global best selection enable the algorithm to escape from local minima stagnation and improve its convergence rate resulting in best optimal parameters. Purpose. The main objective of the proposed approach is to improve the transient response of the automatic voltage regulator system by minimizing the maximum overshoot, settling time, rise time, and peak time values of the terminal voltage, and eliminating the steady-state error. Methods. In the process of parameter tuning, the Chaotic particle swarm optimization technique was run several times through the proposed hybrid objective function, which accommodates the advantages of the two most commonly used objective functions with a minimum number of iterations, and an optimal PID gain value was found. The proposed algorithm is compared with current metaheuristic algorithms including conventional particle swarm optimization, improved kidney algorithm, and others. Results. For performance evaluation, the characteristics of the integral of time multiplied squared error and Zwe-Lee Gaing objective functions are combined. Furthermore, the time-domain analysis, frequency-domain analysis, and robustness analysis are carried out to show the better performance of the proposed algorithm. The result shows that automatic voltage regulator tuned with the chaotic particle swarm optimization based PID yield improvement in overshoot, settling time, and function value of 14.41 %, 37.91 %, 1.73 % over recently proposed IKA, and 43.55 %, 44.5 %, 16.67 % over conventional particle swarm optimization algorithms. The improvement in transient response further improves the automatic voltage regulator system stability for electrical power systems.

This paper aims to develop a Proportional Integral Derivative (PID) controller for the Automatic Voltage Regulator (AVR) of the Synchronous Generator, Automatic Voltage Regulator is responsible for keeping the output voltage of the Generator constant and stable. The Automatic Voltage Regulator system can be represented by a fourth order Transfer Function. The parameters of the controller are evaluated by using the Genetic Algorithm tuning method, for this method the Genetic Algorithm Optimization toolbox in MATLAB is used. A PID controller and a PID Filter controller are developed in order to come up with the optimum controller design for the system. The performance of the Genetic Algorithm based controller has been compared with the classical Ziegler-Nichols based controller. The results show that the Genetic Algorithm based controller outperforms the Ziegler-Nichols based controller, the optimum controller design is achieved by the GA based PID filter controller.

In this paper, an optimal parameter set tuning method for proportional-integral-derivative (PID) controller and fractional order PID controller is proposed using teaching-learning based optimization (TLBO) algorithm. During the optimization of the PID and FOPID controller parameters, an objective function consisting of overshoot, rise time, settling time and steady state error is formulated to achieve a satisfactory trade-off between the dynamic response characteristics. TLBO algorithm is used as the optimizer to find the best parameters of the proposed controllers. The designed PID and FOPID controllers are applied to an automatic voltage regulator (AVR) system. The performances of the proposed controllers are confirmed by comparing their results with those obtained from different optimized PID and FOPID controllers previously published in the literature for the same AVR system. The numerical simulations and comparisons show that the proposed controllers provide the better dynamic response characteristics as well as more robust to model uncertainties than the other different optimized controllers. The results obtained with the proposed controllers show a better trade-off between the set point tracking performance, robustness and stability.

This paper combines the simulated annealing (SA) and white shark optimisation (WSO) algorithms to create a novel hybrid metaheuristic algorithm named SA–WSO. The proposed algorithm finds the best approach to tuning the parameters of three types of regulators. Three types of proportional–integral–derivative (PID) controllers are utilized for an automatic voltage regulator (AVR) system, including fractional–order (FOPID) –controllers. A novel filtered fractional–order PID controller is also presented to enhance the response of the AVR system. The simulation results demonstrate the highly effective optimisation of three controllers, including the rarely used filtrated–FOPID controller, outperforming other controller approaches in the state of the art. The controller performance analyses are also conducted to demonstrate the superiority of SA–WSO-optimized for tuning the controller parameters in different conditions for disturbance rejection and robustness. A comprehensive comparative analysis is performed to determine the most appropriate controller for the AVR system, and it is found that hybrid SA–WSO algorithm provides significant advantages in terms of improved convergence speed.

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.

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

In this paper, a real-valued genetic algorithm (RGA) and a particle swarm optimization (PSO) algorithm with a new fitness function method are proposed to design a PID controller for the Automatic Voltage Regulator (AVR) system. The proposed fitness function can let the RGA and PSO algorithm search a high-quality solution effectively and improve the transient response of the controlled system. The proposed algorithms are applied in the PID controller design for the AVR system. Some simulation and comparison results are presented. We can see that the proposed RGA and PSO algorithm with this new fitness function can find a PID control parameter set effectively so that the controlled AVR system has a better control performance.

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.

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.