Deep Graph Convolutional Autoencoder With Conditional Normalizing Flow for Power Distribution Systems Fault Classification and Location

Abstract

Accurate fault classification and location are critical to ensure the reliability and resilience of large-scale power distribution systems (PDSs). The existing data-driven works in this area struggle to capture essential space-time correlations of PDS measurements and often rely on deterministic and shallow neural architectures. Furthermore, they encounter challenges such as over-smoothing and the inability to capture deep correlations. To overcome these limitations, a novel deep space-time generative graph convolutional autoencoder (SGGCA) is proposed. First, the PDS is modeled as a space-time graph where the nodes and edges show the bus measurements and line impedance values, respectively. The proposed SGGCA's encoder captures deep correlations of the space-time graph using a new graph convolution with early connections and identity transformations to mitigate the over-smoothing. Our encoder encompasses a new recurrent method to adjust graph convolution parameters without relying on node embeddings on the temporal dimension. Additionally, it incorporates generative modeling by capturing the probability distribution function of the latent representation through a conditional normalizing flow model. The extracted generative space-time features are enhanced by a multi-head attention mechanism to better capture task-relevant characteristics of the PDS measurements. The extracted features are fed to sparse decoders to classify and locate the faults in the PDS. The feature sparsity of decoders ensures a high generalization capacity and avoids overfitting. The proposed method is evaluated on the IEEE 69-bus and 123-bus systems. It achieves substantial improvements in fault classification accuracy by 3.33% and 6.26% and enhances fault location accuracy by 6.33% and 5.73% for the respective PDSs compared with state-of-the-art models.