A novel probabilistic framework with interpretability for generator coherency identification

The increase of penetration of renewable energies has posed inevitable challenges to the stability and safety of power system operations, especially in large-scale multi-machine power systems. Emergency control is thereby crucial to avoid catastrophic accidents, and identifying coherent generators is the basis of wide-area control of a multi-machine power system. However, existing approaches are rule-based or rely on shallow machine learning, lacking effectiveness and robustness due to their insufficient ability of pattern mining from system monitoring indicators. To fill the gap, this paper proposes a novel end-to-end generator coherency identification framework, leveraging an improved auto-encoder to comprehensively exploit information of phasor measurement units (PMUs) obtained from wide-area measuring systems (WAMS). The framework jointly trains the feature extraction module and the clustering module to fully explore the shared knowledge and obtain cluster-specific representations. In addition, a visualization component is equipped with the process-agnostic framework for interpretability. Simulated and practical case studies validate the effectiveness of the proposed approach as it outperforms both deep learning baselines and state-of-the-art methods on all datasets under various situations, including observation window size changes, noisy data, or data missing at random. [Display omitted] •An end-to-end framework for probabilistic identification of coherent generators.•Spatial–temporal auto-encoder for informative PMU pattern extraction.•Multi-task learning is implemented for cluster-specific representation learning.•Feature regions of interest are visualized for interpretability.•Case studies validate the effectiveness and robustness of the approach.