Mitigating Multi-Stage Cascading Failure by Reinforcement Learning

08/19/2019 ∙ by Yongli Zhu, et al. ∙ 0 ∙ share

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 presented and its challenges are investigated. The problem is then tackled by the RL based on DC-OPF (Optimal Power Flow). Designs of the key elements of the RL framework (rewards, states, etc.) are also discussed in detail. Experiments on the IEEE 118-bus system by both shallow and deep neural networks demonstrate promising results in terms of reduced system collapse rates.

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Mitigating Multi-Stage Cascading Failure by Reinforcement Learning

Authors

Training Agents using Upside-Down Reinforcement Learning

Learning to Prune Deep Neural Networks via Reinforcement Learning

An analysis of Reinforcement Learning applied to Coach task in IEEE Very Small Size Soccer

Reinforcement Learning for Active Flow Control in Experiments

Knowledge Sharing for Reinforcement Learning: Writing a BOOK

Solving the k-sparse Eigenvalue Problem with Reinforcement Learning

Safer Deep RL with Shallow MCTS: A Case Study in Pommerman

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