A Data-Driven Case Generation Model for Transient Stability Assessment Using Generative Adversarial Networks

Online transient stability assessment (TSA) is crucial for ensuring the security of modern grids. However, problems with limited sample sizes and data imbalance hinder the performance of data-driven TSA classifiers. Addressing this challenge, this article presents a generative adversarial network (GAN)-based model to generate instability samples. Unlike existing methods, our approach moderately alters the long-tailed distribution of instability moments within the raw dataset, producing a more diverse database for TSA tasks. A convolutional neural network-based supervised model, mapping the relationship between fault-clearance electrical characteristics and instability moments of the power system, is incorporated to drive the GAN model to generate realistic yet rare instability cases. Numerical results have proven the superiority of the proposed model over existing case generation methods in terms of realistic and diverse sample generation. In addition, the effectiveness of the proposed data augmentation scheme is demonstrated for online TSA applications.