Research on Optimization Algorithm of Self-Healing Control Strategy of Substation Based on Flexible Interconnection

This paper proposes an optimization scheme that integrates extreme gradient boosting (XGBoost) and reinforcement learning algorithm, and designs a self-healing control strategy for flexible interconnected substations. Firstly, a flexible interconnection model of each node in the substation is constructed, and the network stability under different fault conditions is analyzed. Subsequently, the extreme gradient boosting algorithm is introduced to train the model through large-scale historical data, so as to predict key parameters such as voltage and load in different substations under abnormal conditions. In the simulation test, real substation operation data is used to simulate a variety of fault scenarios to verify the effectiveness of the strategy. The results show that the algorithm not only improves the accuracy of fault location and isolation, but also significantly shortens the recovery time and achieves a self-healing success rate of more than 90%. Through precise data analysis, the practicality of the algorithm in complex power grid environments is verified, reflecting its advantages in improving the self-healing ability of the system, reducing power outage time and improving power supply reliability.