Robust fault diagnosis in power distribution systems based on fuzzy ARTMAP neural network-aided evidence theory

The present study proposes a methodology for the automatic diagnosis of short-circuit faults in distribution systems using modern techniques for signal analysis and artificial intelligence. This support tool for decision making accelerates the restoration process, providing greater security, reliabi...

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Bibliographic Details
Published inIET generation, transmission & distribution Vol. 6; no. 11; pp. 1112 - 1120
Main Authors DECANINI, J. G. M. S, TONELLI-NETO, M. S, MINUSSI, C. R
Format Journal Article
LanguageEnglish
Published Stevenage Institution of Engineering and Technology 01.11.2012
The Institution of Engineering & Technology
Subjects
Applied sciences
Brazil
Electrical engineering. Electrical power engineering
Electrical power engineering
Exact sciences and technology
Miscellaneous
Power networks and lines
Testing. Reliability. Quality control
Waveform
Profitability
Adaptive resonance theory
Decision support system
Discrete wavelet transforms
Fuzzy neural nets
Multiresolution analysis
Power distribution systems
Statistical method
Short circuit
Wavelet transformation
Signal processing
Feature extraction
Safety
Diagnostic aid
Reliability
Signal analysis
Artificial intelligence
Distribution network
Fault diagnostic
Defect detection
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Summary:The present study proposes a methodology for the automatic diagnosis of short-circuit faults in distribution systems using modern techniques for signal analysis and artificial intelligence. This support tool for decision making accelerates the restoration process, providing greater security, reliability and profitability to utilities. The fault detection procedure is performed using statistical and direct analyses of the current waveforms in the wavelet domain. Current and voltage signal features are extracted using discrete wavelet transform, multi-resolution analysis and energy concept. These behavioural indices correspond to the input vectors of three parallel sets of fuzzy ARTMAP neural networks. The network outcomes are integrated by the Dempster-Shafer theory, giving quantitative information about the diagnosis and its reliability. Tests were carried out using a practical distribution feeder from a Brazilian electric utility, and the results show that the method is efficient with a high level of confidence. [PUBLICATION ABSTRACT]
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ISSN:1751-8687
1751-8695
DOI:10.1049/iet-gtd.2012.0028