Research on overload fault early warning method for large-capacity pumped storage units

This study presents a novel overload fault early warning method for large-capacity pumped storage units. By continuously monitoring and analyzing critical operational parameters such as voltage, current, and temperature, and applying sophisticated data analysis and fault diagnosis techniques, the proposed method enables real-time detection and prediction of equipment anomalies. The Composite Detection Index (CDI) is introduced to assess the health status of the units. The Long ShortTerm Memory (LSTM) network is utilized for managing time-series data, enhancing fault prediction accuracy. Experimental results demonstrate the method’s effectiveness in early warning of overload faults, ensuring operational safety and reliability of pumped storage units. The proposed approach provides both theoretical foundations and practical recommendations for the secure operation of high-capacity pumped storage systems, contributing significantly to grid stability and energy efficiency. This research underscores the importance of advanced monitoring and predictive maintenance in modern power systems.