Transformer condition assessment using dissolved gas analysis, oil testing and evidential reasoning approach

Transformer condition monitoring and assessment is crucial as it can be applied to evaluate its maintenance needs. The current trend is to leave critical power transformers in service uninterrupted unless the monitored parameters indicate otherwise. This ensures optimal use of the facility and uninterrupted power supply to consumers. This paper presents the evaluation of the health status of a transformer using the dissolved gas analysis (DGA), oil testing and evidential reasoning criterion. In the analysis, the dissolved gases and oil testing parameters are first normalized and then transformed into fuzzy variables using trapezoid membership function. Then from fuzzy variables, the condition assessment is transformed into a multiple-attribute decision making (MADM) problem under an evidential reasoning (ER) framework. Two case studies are evaluated using actual field data to verify the methodology. Both demonstrate the applicability of the proposed methodology. This emerges as a good logical and systematic way of assessing the maintenance needs of a power transformer using operational data.