Abstract

After a power outage occurs, the power grid recovery can be accelerated according to the reasonable recovery path. This paper proposes a recovery path optimization method based on reinforcement learning. This method can solve complex problems in a model less way and improve the efficiency of the method. The goal is to restore maximum power to the grid. The constraints include over voltage, power flow, frequency, and self-excitation. Through continuous interactive learning between the agent and the power grid during the execution of the recovery path, the Q-value function of the power grid state and the recovery path was obtained. Based on IEEE system data simulation, the effectiveness and rationality of the proposed method are verified.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.