Improving PID Controller Performance in Nonlinear Oscillatory Automatic Generation Control Systems Using a Multi-objective Marine Predator Algorithm with Enhanced Diversity  

作　　者：Yang Yang Yuchao Gao Jinran Wu Zhe Ding Shangrui Zhao 

机构地区：[1]Nanjing University of Posts and Telecommunications,Nanjing,210023,China [2]Australian Catholic University,Banyo,4014,Australia [3]Queensland University of Technology,Brisbane,4001,Australia [4]Wuhan University of Technology,Wuhan,430070,China

出　　处：《Journal of Bionic Engineering》2024年第5期2497-2514,共18页仿生工程学报(英文版)

基　　金：supported in part by the National Natural Science Foundation of China under Grant 61873130;in part by the Chunhui Program Collaborative Scientific Research Project under Grant 202202004;in part by the Foundation of the Key Laboratory of Industrial Internet of Things and Networked Control of the Ministry of Education of China under Grant 2021FF01;in part by the Natural Science Foundation of Nanjing University of Posts and Telecommunications under Grant NY221082,Grant NY222144,and Grant NY223075;in part by the Huali Program for Excellent Talents in Nanjing University of Posts and Telecommunications;in part by the Postgraduate Research and Practice Innovation Program of Jiangsu Province under Grant;in part by the Fundamental Research Funds for the Central Universities under WUT:104972024KFYjc0072.

摘　　要：Power systems are pivotal in providing sustainable energy across various sectors.However,optimizing their performance to meet modern demands remains a significant challenge.This paper introduces an innovative strategy to improve the opti-mization of PID controllers within nonlinear oscillatory Automatic Generation Control(AGC)systems,essential for the stability of power systems.Our approach aims to reduce the integrated time squared error,the integrated time absolute error,and the rate of change in deviation,facilitating faster convergence,diminished overshoot,and decreased oscillations.By incorporating the spiral model from the Whale Optimization Algorithm(WOA)into the Multi-Objective Marine Predator Algorithm(MOMPA),our method effectively broadens the diversity of solution sets and finely tunes the balance between exploration and exploitation strategies.Furthermore,the QQSMOMPA framework integrates quasi-oppositional learning and Q-learning to overcome local optima,thereby generating optimal Pareto solutions.When applied to nonlinear AGC systems featuring governor dead zones,the PID controllers optimized by QQSMOMPA not only achieve 14%reduction in the frequency settling time but also exhibit robustness against uncertainties in load disturbance inputs.

关 键 词：Multi-objective optimization Automatic generation control PID controller Multi-objective marine predator algorithm Whale optimization algorithm 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]