NDLI: Fractal-ANN Tool for Classification of Impulse Faults in Transformers

Content Provider	IEEE Xplore Digital Library
Author	Koley, C. Purkait, P. Chakravorti, S. Brahma, D. Ghanti, M. Pratihar, B. Saha, S.
Copyright Year	2005
Description	Author affiliation: Instrumentation Engineering Department of Haldia Institute of Technology, Haldia, West Bengal. e-mail: chiranjib@ieee.org (Koley, C.)
Abstract	Transformers are impulse tested in laboratory to assess their insulation strength against atmospheric lightning strikes. Inadequate insulation may cause transformer winding to fail during such tests. Detection of such faults is an important issue for repair and maintenance of such transformers. This paper describes the application of the concept of fractal geometry to obtain the features inherent in the impulse response of transformers subjected to impulse test. Fractal features such as fractal dimension (calculated by Higuchi, Kartz, Petrosian and Box counting methods), lacunarity and entropy has been used for collection of proper features from the current waveforms. Artificial Neural Network (ANN) has been used to classify the patterns inherent in the features extracted from Fractal analysis. The complex nature of transformer winding and its impulse response gives rise to a complex non-linear pattern of fractal features. In this regard, the application of ANN for pattern classification has greatly reduced the complexity and at the same time increased the accuracy in the fault localization and identification system. The proposed tool has been tested to identify the type and location of faults by analyzing experimental impulse responses of analog and digital transformer models.
Starting Page	152
Ending Page	156
File Size	1512590
Page Count	5
File Format	PDF
ISBN	0780395034
DOI	10.1109/INDCON.2005.1590144
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-12-11
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fractal Dimension Insulation testing Windings Laboratories Artificial neural networks Fractals Entropy Transformer Model Fault diagnosis Fault detection Artificial Neural Network Lightning Impulse testing Lacunarity Impulse Test Power transformer insulation
Content Type	Text
Resource Type	Article

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