Expert System Based Fault Detection of Power Transformer
The most common diagnosis method for transformer faults is dissolved gas analysis (DGA). The transformer insulation (oil/paper) under abnormal electrical, mechanical or thermal stress dissociates to produce small quantities of gases. These gases are analysed for their composition, using various conventional methods, to detect the fault type in power transformers. In case of multiple fault diagnosis by DGA, the mixing of different characteristic gases results in a ratio code, which cannot be matched by the existing ratio codes, defined by various methods e.g., Rogers ratio method, Dorenberg ratio method, IEC 605999 method etc. To overcome this major drawback of DGA, the transformer diagnostics based on expert systems has been developed. This paper proposes different neural network architectures and fuzzy logic technique, as a diagnostic tool for the fault detection of transformer, using dissolved gas analysis method.
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Document Type: Research Article
Publication date: 2015-02-01
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- Journal of Computational and Theoretical Nanoscience is an international peer-reviewed journal with a wide-ranging coverage, consolidates research activities in all aspects of computational and theoretical nanoscience into a single reference source. This journal offers scientists and engineers peer-reviewed research papers in all aspects of computational and theoretical nanoscience and nanotechnology in chemistry, physics, materials science, engineering and biology to publish original full papers and timely state-of-the-art reviews and short communications encompassing the fundamental and applied research.
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