Real-time detection of islanding and electromechanical oscillations in power systems via clustering in low-rank subspace

Monitoring electromechanical oscillations and islanding is crucial for ensuring the stability of modern power systems, particularly with the increasing penetration of inverter-based resources. Existing model-based and data-driven methods have shown potential but face challenges in robustness and computational efficiency, limiting their real-time application in TSO’s control rooms. This study proposes a novel data-driven approach for detecting electromechanical oscillations and network islanding in large-scale power systems. The method leverages only streaming frequency measurements from Phasor Measurement Units (PMUs) and employs advanced techniques, including Model Order Reduction technique and clustering algorithm such as Hierarchical Agglomerative Clustering. This enables the identification of disconnected network areas or coherent oscillatory regions with high accuracy, robustness, and computational efficiency. The approach is validated through real-world case studies in the European grid, including normal operation, the 2021 Continental Europe Synchronous Area separation, and the December 2016 oscillatory event. Results confirm its suitability for real-time stability monitoring.