Design and robustness analysis of fuzzy PID controller for automatic voltage regulator system using genetic algorithm

Abstract

In this paper, a Fuzzy proportional–integral–derivative (Fuzzy PID) controller design is presented to improve the automatic voltage regulator (AVR) transient characteristics and increase the robustness of the AVR. Fuzzy PID controller parameters are determined by a genetic algorithm (GA)-based optimization method using a novel multi-objective function. The multi-objective function, which is important for tuning the controller parameters, obtains the optimal solution using the Integrated Time multiplied Absolute Error (ITAE) criterion and the peak value of the output response. The proposed method is tested on two AVR models with different parameters and compared with studies in the literature. It is observed that the proposed method improves the AVR transient response properties and is also robust to parameter changes.

Improved Whale Optimization Algorithm for Transient Response, Robustness, and Stability Enhancement of an Automatic Voltage Regulator System

“…The major components of AVR, such as generator, sensor, exciter, and amplifier, are treated as linear components to ease their transfer function modeling process. As such, the transfer function of an amplifier is characterized by a time constant (T A ) and a gain (K A ), as depicted in Equation ( 1) [11].…”

“…Likewise, Equation (2) represents the transfer function of exciter using a time constant (T E ) and a gain (K E ) [11].…”

“…Similarly, the transfer function model of the generator can be depicted in Equation ( 3) with a time constant (T G ) and a gain (K G ) [11].…”

“…The sensor can also be represented by a time constant (TS) and gain (KS) using a firstorder system equation as depicted in Equation ( 4) [11] ( ) 1…”

“…The major problems associated with ANN include the lengthy and time-consuming training process, while in the FL system, the generation of fuzzy membership functions depends on the proper tuning of the model, data analysis, and knowledge of the operator [8]. To avoid the stated issues, in recent years, researchers have employed recently introduced AI-based optimization techniques to achieve the optimal combination of PID controller gains in AVR systems, among which PSO is an extensively used algorithm owing to its smooth optimization process and simple implementation procedure, as can be seen in references [9][10][11][12]. However, it suffers from a few major drawbacks such as slow convergence in the optimization process, stagnation in the local optima, uncertainty in its parameter selection, and sub-optimal response in some of the very familiar benchmark functions [13].…”