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《Automation of Electric Power Systems》 2005-13
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Prediction of Power Transformer Faults Based on Time Series of Weighted Fuzzy Degree Analysis

ZHOU Li-jun , WU Guang-ning , ZHANG Xing-hai, ZHU Kang (Southwest Jiaotong University, Chengdu 610031, China) (Sichuan Provincial Electric Power Company, Chengdu 610041, China) (Electric Testing Institute of Sichuan Province, Chengdu 610072, China)  
This paper analyzes the characteristics of insulation faults caused by gases dissolved in oil. The fuzzy sets of faults phenomena as well as the standard model bases of faults types are then set up. According to the subsets of faults phenomena, several fuzzy vectors are formed. The fault data of many faulty transformers are collected, and the parameters of the faults relative membership degrees (FRMD) function are estimated by statistic method. The FRMD are predicted using time series analysis technique based on gray theory. An improved threshold value principle is proposed and used to predict faults types and the trend of their development. Test results show that the prediction model is valid.
【Fund】: 铁道部科技开发资助项目(2002J036) 电力设备电气绝缘国家重点实验室开放课题资助项目(2001-2)~~
【CateGory Index】: TM407
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