Research on Fast Fault Location and Recovery System in Smart Grid Emergency Response System

This paper presents an innovative "end-to-end" reinforcement learning approach to address the core challenges in smart grid emergency response systems, which aims to synergistically optimize inventory control and emergency power delivery strategies to achieve an optimal balance between maintenance costs and power system stability. The system designed in this study uses a reinforcement learning framework to learn optimal strategies directly from raw input data through deep neural networks, without the need to manually design intermediate features. The method can analyze the state of the power grid in real time, locate the fault point quickly when the fault occurs, and dynamically adjust the distribution of stock and emergency power to minimize the impact of the fault on the power grid operation. Through extensive simulation experiments, the proposed "end-to-end" reinforcement learning algorithm shows excellent performance. Compared with the widely recognized benchmark algorithms in the industry, the average performance of the algorithm improved by a staggering 96.32%. This result not only significantly reduces the fault recovery time, but also effectively controls the maintenance cost and ensures the stable operation of the power grid.