Predictive Maintenance for Transformers and Substation Equipment Using Sensor Time-Series Models

Purpose: Substation equipment and power transformers constitute vital parts of the modern power systems, and its proper functioning is the key to the system stability, efficiency, and safety. Sensors predictive maintenance uses predictive maintenance based on sensor data which is a time-series of data to propose proactive methods to monitor asset health, anomaly detection and predictive maintenance, thus minimizing unplanned outages and maximizing maintenance schedules.

Materials and Methods: This review gives an overall overview of sensor technologies, data features and modeling techniques used in predictive maintenance of transformers and substation equipment. The classical models of statistics, machine learning methods, and deep learning systems are addressed in terms of condition monitoring, anomaly detection, and remaining useful life estimation. Problems such as data quality, model interpretability and deployment concerns are discussed and future research directions such as digital twins, physics-informed models, Edge-AI and secure cloud-edge are identified to inform the further development of the field.

Findings: Additionally, the review highlights predictive models for estimating the remaining useful life (RUL) of assets to optimize maintenance planning.

Unique Contribution to Theory, Practice, and Policy: This review provides a comprehensive understanding of predictive maintenance techniques for transformers and substation equipment. It contributes to theory by summarizing and evaluating various models and methods used in the field. In practice, it offers insight into current and future technologies for asset management and maintenance. The identification of future research areas like digital twins, Edge-AI, and secure cloud-edge will help to drive future developments and influence policy in the power systems sector.