Adaptive PID Control for Hydraulic Turbine Regulation Systems Based on INGWO and BPNN  

作　　者：Jinbao Chen Gang He Yunhe Wang Yang Zheng Zhihuai Xiao 

机构地区：[1]the China Yangtze River Electric Power Co.,Ltd.,Wuhan 430000,China [2]the School of Power and Mechanical Engineering,Wuhan University,Wuhan 430072,China [3]the Hebei Fengning Pumped Storage Co.,Ltd.,Chengde 050300,China

出　　处：《Protection and Control of Modern Power Systems》2024年第4期126-146,共21页现代电力系统保护与控制（英文）

基　　金：supported by the National Natural Science Foundation of China(No.51979204 and No.52009096);the Hubei Provincial Natural Science Foundation of China(No.2022CFD165);the China Postdoctoral Science Foundation(No.2022T150498).

摘　　要：To ensure system stability,the fixed-PID(F-PID)controller with small parameters is usually adopted in hydropower stations.This involves a slow setting speed and it is difficult to realize optimal control for full working conditions.To address the problem,this paper designs a variable-PID(V-PID)controller for a hydraulic turbine regulation system(HTRS)based on the improved grey wolf optimizer(INGWO)and back propagation neural networks(BPNN).These can achieve excellent regulation under full working conditions.First,the nonlinear HTRS model containing the nonlinear hydroturbine model is constructed and the stable domain is obtained using Hopf bifurcation theory to determine the available range of PID parameters.The optimal PID parameters in typical working conditions are then calculated by the INGWO,and the optimal PID parameters are generalized through training the V-PID neural networks which take the optimal PID parameters as sample data.The V-PID neural networks with different structures are compared to determine the optimal structure of the variable-PID controller model.The V-PID controller-based nonlinear HTRS model shows that the PID parameters can be automatically adjusted online according to the working condition changes,realizing optimal control of hydropower units in full working conditions.

关 键 词：Hydraulic turbine regulation system back propagation neural networks Hopf bifurcation grey wolf optimizer variable-PID controller 

分 类 号：TK730[交通运输工程—轮机工程]