Distributed composite learning control for power systems based on emotional self-structuring neural network
SHI Tong-xin
CHEN Long-sheng
REN Yong
Abstract：To solve the control problem of nonlinear multi-agent power systems(NMAPSs)with inevitable nonlineari-ties,uncertainties and dynamic disturbances,a distributed composite learning control is proposed based on the continuous emotional self-structuring neural networks(CESSNNs).Firstly,the unknown nonlinearities of the system are approximated by CESSNNs.Furthermore,a series-parallel identification model is designed to obtain the model identification error of the power system.Moreover,a distributed composite learning control methodology for NMAPSs is proposed based on the outputs of CESSNNs and model identification errors.The closed-loop system signal converges to zero which is proved based on the Lyapunov stability theory.Finally,the simulation results verify the effectiveness of control strategy,and the power system has good robustness and stability.
Keywords：power systemmulti-agentemotional self-structuring neural networkcomposite learning control
Publication Date：2025-02-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：11( 344-354 )