Artificial Intelligence for Diagnosing Power Transformer Faults

Power transformers play a vital role in ensuring the reliability of power and electricity systems, guaranteeing continuous and uninterrupted power supply. Improving the performance, longevity, and reliability of power transformers depends on diligent maintenance practices and the use of suitable methods. Maintenance should be done regularly and carefully to minimize growth and damage to internal and external surfaces. To facilitate standardized and accurate inspections, industry standard provide well-defined methods for measuring the probability of failure and calculating the remaining life of variable power This standard is standards for evaluating transformer health and performance, providing guidance on maintenance practices, and encouraging systematic improvement reliability. This review paper scrutinizes tests aimed at detecting chemical and electrical faults in electrical transformers. These tests incorporate well-established techniques such as dissolved gas analysis, frequency response analysis, and partial discharge (PD) analysis. In addition to these traditional approaches the paper dives into the artificial intelligence (AI) techniques used by researchers in transformer fault diagnosis.