NDLI: Comparison of PD classification capabilities for transformer failure and typical noise models with neural network applications

Content Provider	IEEE Xplore Digital Library
Author	Jin, X.H. Wang, C.C. Cheng, T.C. Li, F.Q. Dong, X.Z. Lei Jiang Zhu, D.H.
Copyright Year	2000
Description	Author affiliation: Univ. of Southern California, Los Angeles, CA, USA (Jin, X.H.)
Abstract	This paper presents three kinds of Neural Networks (NNs) for classifying Partial Discharges (PDs) of failure models which are extracted from internal insulation configurations of power transformers and typical noise in substations. The test results show that three networks can fairly classify the designed models. The performance of Back-Propagation (BP), Learning vector Quantization (LVQ) and Fuzzy ARTMAP networks is evaluated. The classification accuracies obtained from the three networks are compared with each other. In addition to the classification accuracy, the neural networks are analyzed for their generalization capability and stability of the results. Best results (accuracy and convergence time) are obtained with the Fuzzy ARTMAP network. Classification rate for the designed models is 100% at voltage level 3. Simulation results serve to illustrate the properties of various networks we used as well as the stability with respect to various critical parameters.
Starting Page	288
Ending Page	291
File Size	370048
Page Count	4
File Format	PDF
ISBN	0780364139
DOI	10.1109/CEIDP.2000.885283
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2000-10-15
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Partial discharges Power transformers Power transformer insulation Neural networks Substations Testing Vector quantization Stability analysis Convergence Voltage
Content Type	Text
Resource Type	Article

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