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.

READ FULL TEXT

Training Agents using Upside-Down Reinforcement Learning

Traditional Reinforcement Learning (RL) algorithms either predict reward...
12/05/2019 ∙ by Rupesh Kumar Srivastava, et al. ∙ 15 ∙ share

read it
Learning to Prune Deep Neural Networks via Reinforcement Learning

This paper proposes PuRL - a deep reinforcement learning (RL) based algo...
07/09/2020 ∙ by Manas Gupta, et al. ∙ 92 ∙ share

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

The IEEE Very Small Size Soccer (VSSS) is a robot soccer competition in ...
11/23/2020 ∙ by Carlos H. C. Pena, et al. ∙ 0 ∙ share

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

Safe reinforcement learning has many variants and it is still an open re...
04/10/2019 ∙ by Bilal Kartal, et al. ∙ 1 ∙ share

read it
Making Sense of Reinforcement Learning and Probabilistic Inference

Reinforcement learning (RL) combines a control problem with statistical ...
01/03/2020 ∙ by Brendan O'Donoghue, et al. ∙ 46 ∙ share

read it
Knowledge Sharing for Reinforcement Learning: Writing a BOOK

This paper proposes a novel deep reinforcement learning (RL) method inte...
09/05/2017 ∙ by Simyung Chang, et al. ∙ 0 ∙ share

read it
Reinforcement Learning for Active Flow Control in Experiments

We demonstrate experimentally the feasibility of applying reinforcement ...
03/06/2020 ∙ by Dixia Fan, et al. ∙ 0 ∙ share

read it

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

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

Making Sense of Reinforcement Learning and Probabilistic Inference

Knowledge Sharing for Reinforcement Learning: Writing a BOOK

Reinforcement Learning for Active Flow Control in Experiments

This week in AI