A fuzzy information approach to integrating different transformer diagnostic methods

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Abstract
Methods for identifying transformer fault conditions include dissolved gas analysis, liquid chromatography, acoustic analysis, and transfer function techniques. Researchers have applied artificial intelligence concepts in order to encode these diagnostic techniques. These attempts have failed to fully manage the inherent uncertainty in the various methods. A theoretical fuzzy information model is introduced. An inference scheme which yields the most consistent conclusion is proposed. A framework is established that allows various diagnostic methods to be combined in a systematic way. Numerical examples demonstrate the developed system.

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