A State-of-the-Art Review on Intelligent Models for Transformer Fault Monitoring and Diagnosis

Abstract

For electrical power systems to remain stable, power transformers must operate dependably. If left unnoticed or identified too late, transformer defects can result in expensive downtime and catastrophic breakdowns. Recent developments in intelligent models for transformer failure monitoring and diagnosis are examined in this cutting-edge review. It looks at how machine learning, deep learning, and expert systems can be combined to detect defects such temperature anomalies, winding deformation, and partial discharges. Examined are the efficacy and scalability of significant methodologies, including hybrid algorithms, data-driven tactics, and Internet of Things (IoT) technologies. The analysis also highlights current constraints, including as data availability and model interpretability, and suggests future research goals to enhance transformer reliability and predictive maintenance practices. This paper aims to provide researchers and practitioners with a comprehensive grasp of intelligent monitoring systems to transform transformer fault management. This work opens up a wide range of possibilities for creating intelligent models that are more accurate and efficient, improving the models' capacity to handle inconsistent or incomplete data, successfully integrating Internet of Things technologies, making models easier to understand, and creating predictive maintenance systems that work. This significantly affects the longevity of transformers, lowers maintenance costs, and boosts electrical power grid dependability.

Keywords

• Transformer
• Finite Elements Method
• Fault diagnosis
• IoT

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