Partial Discharge Pattern Recognition for Cast Resin Current Transformer Using Fuzzy Neural Network

Abstract:

This paper proposes a pattern recognition approach based on the fuzzy neural network for identifying insulation defects of cast resin current transformer (CRCT) arising from partial discharge (PD). Pattern recognition of PD is used for identifying defects causing the PD, such as internal discharge, external discharge, corona, etc. This information is vital for estimating the harmfulness of the discharge in the insulation. The PD patterns are collected by a PD detecting system in the laboratory. Several statistical methods are used on the phase related distributions in this paper to extract the features for recognition. A set of features, used as operators, for each PD pattern is extracted through statistical methods. To verify the proposed approach, experiments were conducted to demonstrate the field-test PD pattern recognition of CRCT models with artificial defects are purposely created to produce the common PD activities of insulators by using feature vectors of field-test PD patterns. The experimental data are found to be in close agreement with the recognized data. The test results show that the proposed approach may achieve quite satisfactory recognition of PD patterns.

• Periodical: Applied Mechanics and Materials (Volume 577)
• Pages: 515-518
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.577.515
• Online since: July 2014
• Authors: Wen Yeau Chang*
• Keywords: Cast Resin Current Transformer, Fuzzy Neural Network (FNN), High-Voltage, Partial Discharge (PD), Pattern Recognition

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