Spatial-temporal adaptive transient stability assessment for power system under missing data

Abstract

Transient stability assessment (TSA) plays an important role in the design and operation of power system. With the widespread deployment of phasor measurement units (PMUs), the machine learning-based method has attracted much attention for its speed and generalization. However, the generalization will deteriorate if some features are missing due to PMU failure. In this paper, a spatial-temporal adaptive TSA method is proposed to handle the missing data issue. By developing an optimal PMU clusters searching model based on temporal feature importance, and by constructing an ensemble mechanism of long short-term memory (LSTM) for the optimal PMU clusters, the spatial-temporal information is utilized adaptively. Therefore, the aim of maintaining the robustness of TSA performance under any possible PMU failure event is achieved. The proposed approach is demonstrated on New England 39-bus power system. Compared with existing methods, the proposed method achieves state-of-art performance in both accuracy and response time under missing data conditions. In addition, the proposed method is more robust in the case of PMU failure than others.