Online Transient Stability Assessment Under Concept Drift: An ARF-Method-Assisted Federated Learning for Data Streams

Transient instability poses a critical challenge to the reliable operation of modern power systems (PSs), often leading to large-scale blackouts. Despite the success of data-driven transient stability assessment (TSA), its practical implementation remains limited by challenges in processing high-speed real-time data streams and preserving data privacy. To address these limitations, this article develops a novel federated adaptive random forest (FedARF) method that integrates federated learning with the adaptive random forest (ARF) model. The proposed decentralized framework incorporates concept drift adaptation mechanisms to accommodate the stochastic and dynamic characteristics of modern PSs. FedARF facilitates distributed knowledge aggregation learned from various heterogeneous local data sensors (clients) to predict and evaluate the TSA status with minimal communication overhead. Comprehensive experiments on the New England 39-Bus system, the IEEE 68-Bus system, and the large-scale ACTIVIgs 25k-Bus system demonstrate the efficiency of the proposed method with an overall accuracy of 99.65%. Compared to traditional centralized forecasting methods, and state-of-the-art models, the proposed approach not only maintains high-prediction accuracy but also enhances data privacy preservation while substantially reducing communication bandwidth requirements.