On Multi-Event Co-Calibration of Dynamic Model Parameters Using Soft Actor-Critic

Maintaining good quality of transient stability models for power system planning and operational analysis is of great importance. Identification and calibration of bad parameters using PMU measurements that work well for multiple events remains a challenging problem. In this letter, we present a novel parameter calibration method based on off-policy deep reinforcement learning (DRL) algorithm with maximum entropy, soft actor critic (SAC), to automatically tune incorrect parameter sets considering multiple events simultaneously, which can save tremendous labor efforts for maintaining model accuracy and complying with industry standards. The effectiveness of the proposed approach is verified through numerical experiments conducted on a realistic power plant model.

Automated summary

The letter introduces a novel parameter calibration method based on off-policy deep reinforcement learning algorithm, which can automatically adjust incorrect parameter sets considering multiple events, saving a significant amount of work and improving model accuracy. The effectiveness of the proposed approach is verified through numerical experiments conducted on a realistic power plant model.