A stacking classifier plus DQN is reported to reach 97.88% prediction accuracy and 100% stabilization success on a public smart grid dataset, but the RL environment is unspecified.
A distributed control approach for enhancing smart grid transient stability and resilience,
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Hybrid ML-RL Approach for Smart Grid Stability Prediction and Optimized Control Strategy
A stacking classifier plus DQN is reported to reach 97.88% prediction accuracy and 100% stabilization success on a public smart grid dataset, but the RL environment is unspecified.