Research on Wearable Discharge Fault Detection Technology for Electrical Equipment within the Audible Range Using Deep Learning

Transformers and other power equipment form the solid foundation of the power grid, and their insulation performance is crucial for the safe and stable operation of the power system. Accurate assessment of the insulation performance of power equipment is essential to ensure the health status of the equipment and prevent potential failures. However, traditional partial discharge detection methods are often constrained by cumbersome shutdown detection processes, sensitivity to electromagnetic interference, and the limitations of manual detection. This paper innovatively adopts a detection method based on audible sound analysis. We utilize advanced sound feature extraction techniques of spectrograms, combined with the powerful learning capabilities of convolutional neural networks, to achieve rapid detection and precise identification of discharge faults in power equipment. It has been successfully deployed on wearable devices such as smartwatches. This enables operators to perform real-time monitoring and intelligent diagnosis of power equipment anytime, anywhere, providing strong technical support for the stable operation of the power system.