Rethink AI-based Power Grid Control: Diving Into Algorithm Design

Autor:	Zhou, Xiren, Wang, Siqi, Diao, Ruisheng, Bian, Desong, Duan, Jiahui, Shi, Di
Rok vydání:	2020
Předmět:	Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	Recently, deep reinforcement learning (DRL)-based approach has shown promisein solving complex decision and control problems in power engineering domain.In this paper, we present an in-depth analysis of DRL-based voltage control fromaspects of algorithm selection, state space representation, and reward engineering.To resolve observed issues, we propose a novel imitation learning-based approachto directly map power grid operating points to effective actions without any interimreinforcement learning process. The performance results demonstrate that theproposed approach has strong generalization ability with much less training time.The agent trained by imitation learning is effective and robust to solve voltagecontrol problem and outperforms the former RL agents. Comment: Accepted by 34th NeurIPS Ml4eng Workshop, 2020
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2012.13026 Zobrazit plný text záznamu View this record from Arxiv