Failure Analysis in High Voltage Power Systems
Summary
Failure analysis in high voltage power systems examines the root causes and progression of faults that compromise system integrity, safety and reliability. Common failure modes include insulation degradation, partial discharges, thermal overloading, corona and arc formation, mechanical fatigue and surge‐induced dielectric breakdown. Analyses combine field measurements, laboratory testing and computational modelling to identify precursors of failure, assess remaining life and guide maintenance strategies. Techniques such as dissolved gas analysis, infrared thermography, ultrasonic detection and electromagnetic simulation provide complementary insights into evolving defects. Modern approaches integrate real‐time condition monitoring with advanced data analytics and machine learning to anticipate faults before catastrophic breakdowns. As grids expand to accommodate renewable generation and cross‐border interconnectivity, rigorous failure analysis underpins resilience, reduces unplanned outages and informs design of next‐generation equipment. The global imperative for uninterrupted power delivery and the rapid deployment of ultra‐high voltage links heighten the need for precise, scalable diagnostic methods and robust prognostic tools.
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Failure Analysis in High Voltage Power Systems publication trend
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Technical terms
Partial discharge: Localised electric discharges within insulation that erode material and signal early insulation failure.
Dissolved gas analysis: Technique analysing gases generated by oil‐immersed equipment to diagnose internal faults.
Thermal overloading: Condition where excessive current elevates component temperature beyond design limits, accelerating ageing.
Condition monitoring: Continuous or periodic measurement of system variables to detect deviations indicative of developing faults.
Dielectric breakdown: Sudden loss of insulating properties under high electric stress, leading to arcing and equipment damage.
References
- A comprehensive evaluation and advancements in dissolved gas analysis for biodegradable oil in photovoltaic solar plants. Results in Engineering (2024).
- Substation equipment temperature prediction based on multivariate information fusion and deep learning network. PeerJ Computer Science (2022).
- Dynamic model for transmission lines maximum disconnection time on wind farm. Ain Shams Engineering Journal (2021).
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