Fault Location in Power Distribution Networks With Massive Missing Data: A Graph-Based Imputation and Contrastive Learning Approach

Accurate fault location is essential for reliable operation of distribution networks, yet it remains challenging due to incomplete data caused by insufficient sensor deployment, sensor malfunctions, and communication failures during fault events. Existing Graph Neural Network (GNN)-based methods often assume complete data availability or resort to zero-fill imputation. This work proposes a two-stage fault location pipeline for data deficiency environments. A training-free graph-based imputation algorithm (GDIA) is proposed, capable of restoring missing values by directly minimizing graph Dirichlet energy without relying on prior data distribution. Additionally, a theoretical bound is established, linking changes in Dirichlet energy to imputation error. Subsequently, GNN models are employed to perform fault location using the imputed data. To address class imbalance arising from data deficiencies, a graph contrastive learning technique is integrated, further enhancing fault location accuracy. Experimental results demonstrate the proposed method's robustness and effectiveness, highlighting its potential to enhance fault location accuracy in practical distribution network applications.