Fault Tracing of Transformer Substation Devices via Event Graph Kernel

This paper focuses on fault tracing and tracing rule mining of transformer substation devices. Current industrial methods use manually defined features or tracing rules. In addition, they need interpretability and consideration of the connection topology of devices. As an attempt to respond to these needs, this paper proposes a framework to model the substation device monitoring data into graphs, using them to trace faults and mine fault tracing rules. Specifically, this paper proposes a modeling method to construct event graphs based on the connectivity topological structure of devices and alarm signals. Then it addresses fault tracing as a graph classification task operated on the designed event graphs. To do this, this paper proposes a Weighted Generalized Weisfeiler–Lehman graph kernel (W‐GWL), integrating a variant of the Weisfeiler–Lehman graph kernel with fault tracing domain expert knowledge. Meanwhile, this paper proposes an association analysis‐based rule mining method to extract fault tracing rules, which is operated on Weisfeiler–Lehman subtrees. The mined rules can be easily interpreted by domain experts, which enhances the interpretability of the framework. Experiments were conducted on real‐world transformer substation data, and the proposed W‐GWL method performed well on precision, recall, and f1‐score. In addition, the mined fault tracing rules match domain experts' logic. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.