LAUSR

For data-driven dynamic stability assessment (DSA) in power systems, learning cases collected from actual historical records appear to be more reliable than those obtained from numerical simulations with an inevitable reality gap. However, due to the scarceness of transient events in practical systems, historical case sets generally encounter the small sample size and class-imbalance problems. To tackle these challenging issues, this article proposes a novel data/model jointly driven framework to generate high-quality cases for power system DSA applications. Model-driven numerical simulations are first utilized for rough case generation, based upon which case refinement is then intelligently carried out via cycle generative adversarial network (CycleGAN) learning. In this data-driven manner, the CycleGAN is able to produce refined cases highly resembling actual historical ones. A long short-term memory-based semisupervised learning scheme is further designed to reliably label all the refined cases. Numerical tests are comprehensively carried out on the realistic Guangdong Power Grid in South China. With only a small and skewed historical case set initially provided, the proposed framework is able to generate highly realistic cases to augment the set and mitigate the class-imbalance issue. These synthetic cases further help derive a more discerning DSA model, which contributes to enhanced reliability and adaptability of online DSA in practical power grids.