Journal of Electrical Engineering and Automation

This paper studies fault diagnosis and self-healing control technologies for power systems based on deep learning, and proposes an intelligent solution to address the increasingly complex fault scenarios in power systems. Through deep learning models, it is possible to automatically extract features from massive power system data, quickly and accurately identify fault types and locations, and implement self-healing control based on diagnostic results, such as automatic load switching and adjustment of the power grid topology. Experiments show that the proposed fault diagnosis and self-healing control method significantly outperforms traditional methods in terms of accuracy, real-time performance, and automation level. It can effectively enhance the stability and intelligence of power systems, providing a solid technical support for the construction of smart grids.

Deep learning; Power system; Fault diagnosis; Self-healing control; Smart grid