Open AccessControl method of power grid topology structure based on reinforcement learning
Author(s) -
Yanhong Yang,
Wei Pei,
Wei Deng,
Hao Xiao,
Hongjian Sun
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/675/1/012073
Subject(s) - reinforcement learning , computer science , grid , control (management) , renewable energy , smart grid , topology control , topology (electrical circuits) , power (physics) , control engineering , artificial intelligence , distributed computing , engineering , electrical engineering , telecommunications , mathematics , wireless network , geometry , key distribution in wireless sensor networks , physics , quantum mechanics , wireless
With the rapid development of renewable energy and power electronics technology, uncertainty, complexity and data accumulation in power system continue to increase. Traditional methods often encounter bottlenecks in solving operational optimization decision-making and control problems. The development of artificial intelligence technology provides new methods and means for solving the problem of intelligent control of power grid topology. The deep reinforcement learning (DRL) method is used to learn from the historical experience data of power grid topology control that can improve the control and decision-making and methods of system operating performance, and solve the huge variables in traditional models. Through the method in this paper, the DRL method can effectively improve the real-time optimization and control ability of the grid topology.