NDLI: Transformer incipient fault diagnosis based on probabilistic neural network

Content Provider	IEEE Xplore Digital Library
Author	Agrawal, S. Chandel, A.K.
Copyright Year	2012
Description	Author affiliation: Department of Electrical Engineering, National Institute of Technology, Hamirpur-177005, and India (Chandel, A.K.) \|\| Department of Electrical Engineering, National Institute of Technology, Hamirpur-177005, India (Agrawal, S.)
Abstract	This paper proposes a technique for transformer fault diagnosis. The proposed technique is a four-layer probabilistic neural network (PNN). The proposed diagnostic technique has faster training capability because it is build with a single pass of exemplar pattern set and without any iteration(epochs) for weight adaptation. This diagnostic technique uses normalized parts per million values of gases (hydrogen (H2), methane (CH4), ethane (C2H6), ethylene (C2H4), acetylene (C2H2)) as an input to detect partial discharge, low energy discharge, high energy discharge, low &medium temperature fault, high temperature transformer faults. The effectiveness of the proposed diagnostic approach is verified on the basis of the experiments on transformer oil dissolve gas samples. The results indicate that the PNN approach can be successfully used for transformer faults diagnosis.
Starting Page	1
Ending Page	5
File Size	281018
Page Count	5
File Format	PDF
ISBN	9781467304566
e-ISBN	9781467304559
DOI	10.1109/SCES.2012.6199110
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-03-16
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault diagnosis Training Transformer Neodymium Neurons Incipient fault Oil insulation Dissolved gas analysis Probabilistic logic Diagnosis Power transformers Probabilistic neural network (PNN)
Content Type	Text
Resource Type	Article

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