Time and frequency domain analyses based expert system for impulse fault diagnosis in transformers

The presence of insulation failure in the transformer winding is detected using the voltage and current oscillograms recorded during the impulse test. Fault diagnosis in transformers has several parameters such as the severity of fault, the kind of fault and the location of the fault. Detection of major faults involving a large section of the coils have never been a big issue and several visual and computational methods have already been proposed by several researchers. The present paper describes an expert system based on re-confirmative method for the diagnosis of minor insulation failures involving small number of turns in transformers during impulse tests. The proposed expert system imitates the performance of an experienced testing personnel. To identify and locate a fault, an inference engine is developed to perform deductive reasoning based on the rules in the knowledge base and different statistical techniques. The expert system includes both the time-domain and frequency-domain analyses for fault diagnosis. The basic aim of the expert system is to provide a non-expert with the necessary information and interaction in order to make fault diagnosis in a friendly windowed environment. The rules for fault diagnosis have been so designed that these are valid for the range of power transformers used in practice up to a voltage level of 33 kV. The fault diagnosis algorithm has been tested using experimental results obtained for a 3 MVA transformer and simulation results obtained for 5 and 7 MVA transformers.