An adaptive assessment method of power system transient stability considering PMU data loss

Abstract

Transient stability assessment (TSA) plays an important role in ensuring the reliable operation of power systems. With the popularity of phasor measurement units (PMUs), data-driven TSA methods have been widely concerned. However, the performance of TSA model may deteriorate when data loss occurs due to PMU failure. This paper proposes an adaptive assessment method for transient stability of power systems considering PMU data loss. First, considering the importance of temporal features, a collection of PMU clusters is constructed to minimize the failure risk and maintain full observability of the whole buses of the grid. Secondly, a weighted integrated assessment model based on PMU clusters is constructed by using an improved eXplainable Convolutional neural network for Multivariate time series classification (XCM) as a TSA classifier. The model can make full use of time series information to carry out adaptive TSA and maintain the robustness of the assessment performance even when PMU failure occurs. Finally, it is verified in a modified IEEE 39-bus system with wind power and solar power. The effect of the proposed method shows high accuracy and strong anti-noise interference ability in case of data loss.