Open AccessResearch on Parameter Identification Method of Generator Excitation System Based on Differential Evolution Algorithm
Author(s) -
Nan Li,
Yuqun Liu,
Bin Geng
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/752/1/012017
Subject(s) - convergence (economics) , algorithm , nonlinear system , stability (learning theory) , identification (biology) , generator (circuit theory) , electric power system , differential evolution , computer science , control theory (sociology) , mathematical optimization , mathematics , power (physics) , artificial intelligence , physics , botany , control (management) , quantum mechanics , machine learning , economics , biology , economic growth
In order to solve the problem of large parameter identification error caused by nonlinear links of excitation system being triggered easily when transient stability is under fault state, an improved differential evolution algorithm for system parameter identification is proposed by using the characteristic of artificial intelligence algorithm that the nonlinear link is approximated infinitely through optimization. The improvement of the algorithm solves the problems of slow convergence speed, poor fine optimization ability and easily to produce local optimum when classical artificial intelligence algorithm identifies the parameters of non-linear links. At the same time, in order to solve the problem of inaccurate parameters in the whole identification, a decomposition link identification strategy is proposed. The example analysis shows that the algorithm improves the convergence speed, avoids local optimum and improves the convergence accuracy. According to the proposed parameter identification strategy, the excitation system is decomposed and identified, which improves the accuracy of generator excitation system parameter identification, and provides an accurate model and method for power system stability analysis