A Gramian Angular Field Transform-Based Higher-Dimension Data-Driven Method for Post-Fault Short-Term Voltage Stability Assessment

Yuchi Tang, Chao Ren, Rui Zhang, Shuo Ren

Abstract: As the complexity of power systems increases, together with the increasing integration of phasor measurement units, an intelligent assessment for short-term voltage stability (STVS) becomes necessary and feasible. Especially for post-fault STVS assessment, the task requires a fast and accurate conclusion. Given this, an intelligent post-fault STVS assessment method is proposed in this research. Based on Gramian Angular Field transform, two-dimensional convolutional neural network, and adaptive confidence interval, the proposed method with an offline-online data-driven framework shows better performance when the amount of input data is limited. The related simulations are conducted on the New England 10-machine 39-bus system.