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3-D Partial Discharge Patterns Recognition of Power Transformers Using Neural Networks

  • Hung-Cheng Chen
  • Po-Hung Chen
  • Chien-Ming Chou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)

Abstract

Partial discharge (PD) pattern recognition is an important tool in HV insulation diagnosis. A PD pattern recognition approach of HV power transformers based on a neural network is proposed in this paper. A commercial PD detector is firstly used to measure the 3-D PD patterns of epoxy resin power transformers. Then, two fractal features (fractal dimension and lacunarity) extracted from the raw 3-D PD patterns are presented for the neural- network-based (NN-based) recognition system. The system can quickly and stably learn to categorize input patterns and permit adaptive processes to access significant new information. To demonstrate the effectiveness of the proposed method, the recognition ability is investigated on 150 sets of field tested PD patterns of epoxy resin power transformers. Different types of PD within power transformers are identified with rather encouraged results.

Keywords

Fractal Dimension Fractal Feature Power Transformer Probabilistic Neural Network Partial Discharge 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hung-Cheng Chen
    • 1
  • Po-Hung Chen
    • 2
  • Chien-Ming Chou
    • 1
  1. 1.National Chin-Yi Institute of TechnologyInstitute of Information and Electrical EnergyTaiping, TaichungTaiwan, R.O.C.
  2. 2.Department of Electrical EngineeringSt. John’s UniversityTaipeiTaiwan, R.O.C.

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