Intelligent Identification Method for Device Action Fre-Quency Based on Data Mining

With the large-scale integration of renewable energy into power grids, the regulation capabilities of compensation devices face challenges due to the instability of renewable energy output. To address this issue, this paper pro-poses an intelligent identification method for device action frequency based on data mining technology. Historical data is collected and combined with renewable energy output forecasts for data mining. The mining results are then refined using a reinforcement learning model based on Deep Q-Networks (DQN). Additionally, Monte Carlo simulations are incorporated to enhance the robustness of the results.