AUTHOR=Li Beibei , Liu Qian , Hong Yue , He Yuxiong , Zhang Lihong , He Zhihong , Feng Xiaoze , Gao Tianlu , Yang Li TITLE=Data-driven active corrective control in power systems: an interpretable deep reinforcement learning approach JOURNAL=Frontiers in Energy Research VOLUME=12 YEAR=2024 URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2024.1389196 DOI=10.3389/fenrg.2024.1389196 ISSN=2296-598X ABSTRACT=

With the successful application of artificial intelligence technology in various fields, deep reinforcement learning (DRL) algorithms have applied in active corrective control in the power system to improve accuracy and efficiency. However, the “black-box” nature of deep reinforcement learning models reduces their reliability in practical applications, making it difficult for operators to comprehend the decision-making mechanism. process of these models, thus undermining their credibility. In this paper, a DRL model is constructed based on the Markov decision process (MDP) to effectively address active corrective control issues in a 36-bus system. Furthermore, a feature importance explainability method is proposed, validating that the proposed feature importance-based explainability method enhances the transparency and reliability of the DRL model for active corrective control.