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	Application of statistic parameters in recognition of partial discharge in transformers
	Hu Wen-tang ; Gao Sheng-you ; Yu Shao-feng ; Tan Ke-xiong ; Gao Wen-sheng
	2010-10-12 ; 2010-10-12
关键词	Practical Theoretical or Mathematical Experimental/ fault diagnosis Monte Carlo methods neural nets partial discharge measurement pattern classification power engineering computing power transformers Weibull distribution/ transformer partial discharge estimation pattern recognition failure diagnosis 2-parameter Weibull statistical distribution PD height distribution Monte-Carlo method cumulative probability artificial neuron network classifier random pulse interference time 10 s/ B8350 Transformers and reactors B2810D Dielectric breakdown and discharges B7310C Charge measurement B0240G Monte Carlo methods C7410B Power engineering computing C5290 Neural computing techniques C1140G Monte Carlo methods/ time 1.0E+01 s
中文摘要	The pattern recognition for partial discharge (PD) is helpful for estimating PD type and level, which will provide a scientific basis for failure diagnosis and maintenance. We used Weibull statistic distribution parameters in the pattern recognition for PD, and adopted the simulation and model test to prove that the PD height distribution (PDHD) fits well with 2-parameter Weibull distribution. The simulation data were generated by the Monte-Carlo method and 8 typical PD models were designed for test. In the period of each test, the pulse amplitude and phase information of each PD pulse in 10 seconds were recorded. Meanwhile, the discharge amplitude was normalized during calculation of the PDHD. In order to eliminate the influence of random pulse interference, the amplitude whose cumulative probability is 99% was used as the normalization factor. Six characteristic parameters, including the shape parameter of Weibull distribution, the center of discharge amplitude and the center of discharge phase in positive half period and negative half period of power cycle, were adopted to characterize fault pattern. The artificial neuron network was used as classifier to get at least 85% recognition rate for PD types. The results show that excellent characterization capability is obtained using a few of characteristic parameters,and different types of PD can be accurately identified using the artificial neuron network.
语种	中文
出版者	Editorial Board of High Voltage Engineering ; China
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/82820]
专题	清华大学
推荐引用方式 GB/T 7714	Hu Wen-tang,Gao Sheng-you,Yu Shao-feng,et al. Application of statistic parameters in recognition of partial discharge in transformers[J],2010, 2010.
APA	Hu Wen-tang,Gao Sheng-you,Yu Shao-feng,Tan Ke-xiong,&Gao Wen-sheng.(2010).Application of statistic parameters in recognition of partial discharge in transformers..
MLA	Hu Wen-tang,et al."Application of statistic parameters in recognition of partial discharge in transformers".(2010).

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