Artificial ecosystem-based optimization for optimal tuning of robust PID controllers in AVR systems with limited value of excitation voltage

Abstract

This paper presents an application of a novel optimization method called Artificial Ecosystem-Based Optimization (AEO) to determine the optimal design parameters of the proportional-integral-derivative (PID) controller for an automatic voltage regulator (AVR) system. Unlike the previous studies presented in the literature, the proposed method takes into account the excitation voltage limit and therefore formulates a new objective function for optimal PID parameters design. The practical aspect of the proposed constraint is significant since the generator field winding can be seriously damaged in case of the large excitation voltage. The performance of the proposed controller using the solution methodology proposed in this study and its contribution to the robustness of the control system are investigated. Further, the obtained PID parameters are used to simulate the AVR dynamics for a large step change in the generator’s voltage set-point. Besides, the obtained step responses have been compared with the corresponding responses of the AVR system whose PID parameters are determined by using well-known methods presented in the literature. Also, the proposed AEO-PID controller shows superior performances in the case of uncertainties in the AVR system’s parameters, as well as in the presence of the different disturbances in the system. The results obtained show that the obtained parameters provided a more secure and stable machine operation even with changes of the reference, generator, or excitation voltage signals compared with the performance of the controllers obtained by the previous works presented in the literature. Furthermore, AEO has proven its ability to get optimal solutions in a fast and efficient manner in terms of accuracy and time spent per iteration.