Bad-Data Sequence Detection for Power System State Estimation via ICA-GAN

A deep learning approach to the detection of bad-data sequences in power systems is proposed. The bad-data model is nonparametric that includes arbitrary natural and adversarial data anomalies. No historical samples of data anomaly are assumed. The probability distribution of data in anomaly-free system operations is also non-parametric, unknown, but with historical training samples. A uniformity test is proposed based on a generative adversarial network (GAN) that extracts independent components of the measurement sequence via independent component analysis (ICA). Referred to as ICA-GAN, the developed approach to bad-data sequence detection can be applied at the individual sensor level or jointly at the system level. Numerical results demonstrate significant improvement over the state-of-the-art solutions for a variety of bad-data cases using PMU measurements from the EPFL smart grid testbed and that from the synthetic Northern Texas grid.