Dynamic Graph Variational Sparse Coding for Power Distribution Network Fault Location and Classification

Fault location and classification are crucial to the reliable and resilient operation of power distribution networks (PDNs). Current machine learning works cannot provide accurate and reliable results for fault detection due to the lack of generalization power, feature sparsity, and capturing the interrelation information of adjacent PDN buses. Therefore, we propose a novel generative dynamic graph variational sparse coding framework for spatiotemporal fault detection. First, the PDN is modeled as a dynamic graph where the nodes represent the load and voltage sequence components of the buses, and edges show the impedances of the PDN lines. A dynamic graph variational autoencoder (DGVAE) is developed to learn the generative spatiotemporal features of the dynamic graph. The DGVAE incorporates a new edge propagation graph convolutional network with an attention mechanism into a deep recurrent model to capture the task-relevant spatial features of the PDN considering the interrelation information of nodes and the temporal features. Finally, a dictionary learning method is devised to learn the deep sparse representation of the captured generative features, which is mapped to the fault locations and classes. The proposed framework demonstrates superior results compared to the state-of-the-art benchmarks on the modified IEEE 69-bus and IEEE 123-bus PDNs in both fault location and classification tasks.