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《AUTOMATION OF ELECTRIC POWER SYSTEMS》 2000-15
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STUDY ON THE OPTIMAL RECLOSING TIME BASED ONWAVELET TRANSFORM AND ARTIFICIAL NEURAL NETWORK

Sun Jing, Li Xingyuan. Li Li(1. Sichuan University. Chengdu 610065. China)(2. University of Bath. UK)  
At present. the methods for calculating the optimal reclosing time for transient faults mostly are off--line. Forexample, the usage of transient energy function is difficult in computation and needs long computational time. which can'tmeet the requirements of rapid change of real operation in power system. An on-- line method for obtaining the optimalreclosing time of transient faults is presented based on wavelet transform and a rtificial neural network. which costs less timeto calculate the optimal reclosing time. At first. power system faults are simul ated using MATLAB. and the signals of faultsthat are transformed by wavelet transform are decomposed into "approximations" a nd "details' at different scales. and theirfeatures which are extracted through wavelet transform are considered to be the inputs for artificial neural network. Thenthey are trained through artificial neural network to find the optimal reclosing time. The validity and accuracy of this methodis testified by test examples.This project is supported by National Key Basic Research Special Fund of China ( No. G1998020312) and National KeyLab of Tsinghua University.
【Fund】: 国家重点基础研究专项经费!(G1998020312);; 清华大学电力系统国家重点实验室资助项目
【CateGory Index】: TM76
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