A Comprehensive Survey On Transformer Fault Diagnosis and Operating Condition Prediction

Transformers are playing an increasingly significant role in electricity energy conversion, transmission, and distribution, which link energy resources such as conventional, renewable and sustainable energy, from generation to consumption. Reliable and precise diagnosis and prediction of operating conditions of power transformers have become continuously important. Until now, several methods for transformer fault diagnosis and operating condition prediction have been applied to various theoretical investigations and actual applications. This paper provides a comprehensive review of the existing works from two perspectives, namely, transformer fault diagnosis and operating condition prediction. The overview of transformer fault diagnosis consists of dissolved gas analysis, acoustic signal processing, and image recognition. On the other hand, the previous studies related to operating condition prediction of the transformer are critically examined and explored according to whether considering the association rules of state parameters. Finally, conclusions and future trends are presented.