Data Augment Using Deep Convolutional Generative Adversarial Networks for Transient Stability Assessment of Power Systems

Real-time and accurate transient stability assessment (TSA) is essential for planning, operation and control of power systems. As a data-driven technology, deep learning method plays an important role in TSA. Nevertheless, the fact that instability situations rarely occur would lead to a challenging class-imbalanced issue, which brings great difficulties to the deep learning methods. Besides, feature extraction from high dimensional input data and transient stability classification seem extremely difficult for conventional classification methods. To address these problems, this paper develops a class-imbalanced TSA method by combining nonlinear data synthesis method with the deep learning classification model. Firstly, deep convolutional generative adversarial network (DCGAN) is conducted to generate unstable instances based on the existing samples to balance the proportion of different classes. Furthermore, the convolutional neural network (CNN) is utilized to extract the nonlinear mapping relationship between the disturbance features and the stability category and realize TSA. Finally, the IEEE 10-machine, 39-bus New England system is utilized to verify the validity and effectiveness of the proposed method.