Spatial-Temporal Feature Learning in Smart Grids: A Case Study on Short-Term Voltage Stability Assessment

The advancing machine learning techniques have been widely applied to data-driven dynamic stability assessment (DSA) in modern smart grids. However, how to extract critical spatial-temporal features from wide-area system stability dynamics still remains an open issue. Emphasizing on short-term voltage stability (SVS) assessment, this paper develops a novel sequential feature learning approach to address this problem in two steps. First, based on visualized voltage contours, it tactfully constructs a comprehensive spatial-temporal sequence model to dynamically characterize multiplex spatial-temporal SVS evolution trends. Second, the time series shapelet classification method is leveraged to subtly extract critical consecutive SVS features in sequential forms, i.e., the multidimensional shapelets (discriminative subshapes). Test results on the real-world Hong Kong power grid demonstrate the efficacy, adaptability, and scalability of the proposed approach for SVS assessment. In addition to the outstanding performances on online DSA, with its favorable interpretability, it is capable of providing intuitive insights into regional SVS patterns from spatial-temporal perspectives.