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Schuet, S., Timucin, D. and Wheeler, K. (2011) A Model-Based Probabilistic Inversion Framework for Characterizing Wire Fault Detection Using TDR. IEEE Transactions on Instrumentation and Measurement, 60, 1654-1663.
https://doi.org/10.1109/tim.2011.2105030

has been cited by the following article:

• TITLE: Fault Identification and Predictive Maintenance Techniques for High-Voltage Equipment: A Review and Recent Advances

  AUTHORS: Emmanouil D. Fylladitakis

  KEYWORDS: High-Voltage Power Systems, Fault Identification, Predictive Maintenance, Condition-Based Monitoring, Artificial Intelligence, IoT, Online Monitoring

  JOURNAL NAME: Journal of Power and Energy Engineering, Vol.13 No.7, July 15, 2025

  ABSTRACT: This paper reviews fault identification and predictive maintenance techniques essential for the reliable operation of high-voltage power systems. The increasing integration of renewable energy sources and distributed generation imposes significant operational challenges, necessitating advanced maintenance strategies beyond traditional reactive and preventive approaches. Emphasis is placed on condition-based and online monitoring techniques that leverage artificial intelligence, machine learning, and IoT for timely fault detection and accurate prediction of equipment lifespan. The study examines methodologies applied to critical high-voltage components such as circuit breakers, transformers, and cables, detailing their operational principles, advantages, limitations, and evaluation metrics. While substantial progress has been made in enhancing system reliability and reducing downtime, challenges related to data quality, model complexity, and cybersecurity remain, highlighting areas for future research and development.