Improved Grey Wolf Algorithm to Optimize SVR for Resistive Current Prediction of Arrester
The magnitude and variation of arrester resistive current under the operating voltage reflect the state of arrester.Aiming at the problem that the data samples of on-line measurement are small,the support vector regression(SVR)optimized by improved grey wolf optimizer(IGWO)is proposed to predict resistive current of arrester.In view of the fact that the resistive current is significantly affected by ambient temperature and interphase interference,the prediction method takes the phase,historical resis-tive current,ambient temperature and temperature difference as characteristic inputs.An improved grey wolf optimizer based on Lo-gistic chaotic mapping for population initialization and nonlinear convergence factor is adopted.Three phase on-line measurement datas of a 500kV arrester in recent 10 years are used for modeling and analysis.The results of calculation example show that the pro-posed method is accuracy and feasible.
prediction of resistive currentimproved grey wolf optimizersupport vector regressionchaotic mappingmetal oxide arrester
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