A Comparison between Artificial Intelligence Method and Standard Diagnosis Methods for Power Transformer Dissolved Gas Analysis Using Two Public Databases

Abstract

Oil‐filled power transformers play an important role in modern network systems. Stable power supply can be achieved by early detection of power transformer faults and continuous monitoring of equipment. In recent years, dissolved gas analysis (DGA) has been widely used to diagnose faults in power transformers. Although DGA is an easier and simpler method for the fault diagnosis of transformers, different techniques usually provide different results with real‐world data. In fact, conventional diagnosis approaches for power transformers depend on human experience and available technology of human experts. Therefore, we propose using an artificial intelligence (AI) technique called multilayer perceptron (MLP) for the intelligent diagnosis of power transformer faults. In this work, the MLP model is constructed using the Keras library. The method is tested using two public databases: one based on the Electric Technology Research Association of Japan (ETRA) database and another based on the IEC TC10 database. Results indicate that high‐prediction accuracy is achieved. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.