Enhancing Transformer Reliability Through Dissolved Gas Analysis and Machine Learning: A Literature Review

Abstract

Reliability of Power Transformers not only affects the supply of energy but also affects the utility due to loss of economic operation. This review paper provides a current state of the reliability of transformers using dissolved gas analysis and machine learning. This paper presents the historical review of the literature, advancements in the field by using the machine learning algorithms, data analytics and their improvement in maintain the reliability of power supply by power transformers. The paper also highlights the benefits and limitations of different approaches used by conventional approaches to rule based systems to machine learning algorithms. The aim of review of this literature is to have valuable insights in to the reliable operation of transformers to ensure the reliable supply of electrical supply and a power industry as a whole.