A DDPG agent that adjusts generator outputs at each stage of simulated multi-stage cascading failures achieves higher win rates than random, full-power, and half-power baselines on IEEE 14-bus and 118-bus grids.
A critical review of cascading failure analysis and modeling of power system
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Deep Reinforcement Learning for Power Grid Multi-Stage Cascading Failure Mitigation
A DDPG agent that adjusts generator outputs at each stage of simulated multi-stage cascading failures achieves higher win rates than random, full-power, and half-power baselines on IEEE 14-bus and 118-bus grids.