Sample Selection Generative Adversarial Networks for Intelligent Frequency Regulation of Microgrids

Large-scale renewable energy integration introduces random power fluctuations into microgrids, increasing the difficulty of frequency regulation. To improve regulation stability and training efficiency, this article proposes sample selection generative adversarial networks (SSGANs) based on sample selection networks (SSNs), conditional generative adversarial networks (CGANs), and the actor–critic framework. First, the SSNs are trained to evaluate sample information values and prioritize informative samples for model training. Second, the CGANs learn the conditional mapping between microgrid operating states and control actions, and the pretrained generator is transferred into the actor–critic framework as the actor. Third, the actor–critic framework further optimizes the control policy online to generate real-time frequency regulation commands. The proposed method is tested on a standard two-area system and further validated on a complex four-area system. Case studies show that SSGANs achieve faster convergence and better frequency regulation performance than typical control algorithms.