Title :
Partial discharge pattern recognition of power transformer with neural network applications
Author :
Xianhe Jur ; Wang, Changclnng ; Jing, Weiliong ; Cheng, T.C. ; Jiang, Lei ; Deheng Zhur ; Li, Fuqi ; Dong, Xuzhu
Author_Institution :
Dept. of Electr. Eng., Univ. of Southern California, Los Angeles, CA, USA
Abstract :
Power transformers play a crucial role in operation of transmission and distribution systems. A dielectric failure in a power transformer could result in unplanned outages of power systems, which affect a large number of customers. In this paper, the application of two different networks to recognize patterns of partial discharge (PD) of power transformer models is studied. Eleven well-designed discharge models are presented as well. According to results of PD measurements and neural network (NN) training and testing, two different NN paradigms can recognize different PD sources for those data at the same voltage level. NN might misclassify those PD patterns or sources with data of different voltage level
Keywords :
backpropagation; learning (artificial intelligence); neural nets; partial discharges; pattern classification; power engineering computing; power transformer insulation; power transformer testing; surface discharges; transformer oil; vector quantisation; PD measurements; backpropagation network; discharge models; external noise models; learning vector quantization network; neural network testing; neural network training; partial discharge pattern recognition; power transformer models; power transformers; pressboard separation; surface discharges; transformer oil; voltage level; Dielectrics; Fault location; Neural networks; Partial discharge measurement; Partial discharges; Pattern recognition; Power system measurements; Power system modeling; Power transformers; Testing;
Conference_Titel :
Electrical Insulation and Dielectric Phenomena, 1999 Annual Report Conference on
Conference_Location :
Austin, TX
Print_ISBN :
0-7803-5414-1
DOI :
10.1109/CEIDP.1999.804640
