A Physics-Informed Neural Network Modeling Approach for Energy Storage-Based Fast Frequency Support in Microgrids
Stichworte:
Training; Adaptation models; Perturbation methods; Power system dynamics; Microgrids; Power system stability; Data models; Microgrids; frequency dynamics; physics-informed neural network; modeling
Kongress- / Buchtitel:
2024 IEEE Electrical Energy Storage Application and Technologies Conference (EESAT)