Networked Time Series Shapelet Learning for Power System Transient Stability Assessment

While many machine learning approaches have been widely applied to power system online dynamic stability assessment, how to sufficiently learn spatial-temporal correlations from system transients without losing the interpretability is still a challenging issue. In this paper, a novel networked time series shapelet learning approach is proposed to learn spatial-temporal correlations for transient stability assessment (TSA) in an interpretable manner. Specifically, a network impedance-based adjacency matrix is first introduced to characterize spatial networked correlations. Based on graph structural regularization, this matrix is effectively incorporated into the subsequent learning procedure as spatial constraints. Taking time series trajectories acquired from multiple buses as the inputs, networked shapelet learning is heuristically performed to learn critical sequential features, i.e., networked shapelets, for TSA model derivation. With the learning procedure strategically guided by inherent spatial-temporal correlations of the system, the obtained data-driven TSA model is able to perform highly reliable and interpretable online TSA. Numerical test results on the IEEE 39-bus test system and the realistic GD Power Grid in China verify the superior performances of the proposed approach.

Automated summary

This paper proposes a networked time series shapelet learning approach for interpretable transient stability assessment (TSA). By introducing a network impedance-based adjacency matrix to characterize spatial networked correlations, and incorporating it as spatial constraints, the method learns critical sequential features, i.e., networked shapelets, from time series trajectories acquired from multiple buses. The obtained data-driven TSA model performs highly reliable and interpretable online TSA, as demonstrated by numerical test results on real-world power systems.