The content discusses the challenges of centralized deep learning models for transient stability assessment in power systems. It introduces a federated approach where local utilities train their own models independently, preserving data privacy and reducing computational requirements. The proposed framework is tested on four local clients using the IEEE 39-bus test system. Various references are cited to highlight the shift towards utilizing advanced DL techniques like CNNs and LSTMs for power system stability assessment. The paper also outlines the procedures for the federated DL-based TSA framework and system stability classification schemes. Results from testing show the effectiveness of the proposed approach in detecting complex system operating states.
다른 언어로
소스 콘텐츠 기반
arxiv.org
더 깊은 질문