Power Grid Cascading Failure Mitigation by Reinforcement Learning

Zhu, Yongli

Power Grid Cascading Failure Mitigation by Reinforcement Learning (Papers Track)

Yongli Zhu (Texas A&M University)

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Disaster Management and Relief Power & Energy

Abstract

This paper proposes a cascading failure mitigation strategy based on Reinforcement Learning (RL) method. Firstly, the principles of RL are introduced. Then, the Multi-Stage Cascading Failure (MSCF) problem is formulated, and its challenges are investigated. The problem is then tackled by the RL based on DCOPF (Direct Current Optimal Power Flow). Designs of the RL framework (rewards, states, etc.) are illustrated in detail. Experiments on the IEEE 118-bus system by the proposed RL method demonstrate promising performance in reducing system collapses.

Recorded Talk (direct link)

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