In this study technical diagnostic tests and economical lifetime assessment of transformers are investigated to evaluate the overall health condition of working transformers. Two artificial intelligence models including artificial neural network and adaptive neuro-fuzzy inference system models are presented to determine the health index for transformers. The technical and economical parameters are used as input parameters to develop the models. Technical parameters are extracted from oil characteristics and dissolved gas analysis of different transformers. Economical parameters are constructed with transformer capital investments, maintenance and operating costs. The models are developed using 226 experimental field datasets of transformers technical and economical parameters. The models are trained using 80% of the experimental datasets. The remaining 20% is used to evaluate the performance and applicability of the models. The results prove that the models can be used to determine the health condition of transformers with high accuracy.

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