DeepAI AI Chat
Log In Sign Up

Power Grid Cascading Failure Mitigation by Reinforcement Learning

by   Yongli Zhu, et al.

This paper proposes a cascading failure mitigation strategy based on Reinforcement Learning (RL). The motivation of the Multi-Stage Cascading Failure (MSCF) problem and its connection with the challenge of climate change are introduced. The bottom-level corrective control of the MCSF problem is formulated based on DCOPF (Direct Current Optimal Power Flow). Then, to mitigate the MSCF issue by a high-level RL-based strategy, physics-informed reward, action, and state are devised. Besides, both shallow and deep neural network architectures are tested. Experiments on the IEEE 118-bus system by the proposed mitigation strategy demonstrate a promising performance in reducing system collapses.


Mitigating Multi-Stage Cascading Failure by Reinforcement Learning

This paper proposes a cascading failure mitigation strategy based on Rei...

Reinforcement Learning for Resilient Power Grids

Traditional power grid systems have become obsolete under more frequent ...

Physics-informed Evolutionary Strategy based Control for Mitigating Delayed Voltage Recovery

In this work we propose a novel data-driven, real-time power system volt...

Power and Interference Control for VLC-Based UDN: A Reinforcement Learning Approach

Visible light communication (VLC) has been widely applied as a promising...

Reactive Failure Mitigation through Seamless Migration in Telecom Infrastructure Networks

Various methods are proposed in the literature to mitigate the failures ...

Power and accountability in reinforcement learning applications to environmental policy

Machine learning (ML) methods already permeate environmental decision-ma...

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 ...