A Concurrent Fault Diagnosis Method of Transformer Based on Graph Convolutional Network and Knowledge Graph
Liqing Liu, Bo Wang, Fuqi Ma, Quan Zheng, Liangzhong Yao, Chi Zhang, Mohamed A. Mohamed
Abstract
In complex power systems, when power equipment fails, multiple concurrent failures usually occur instead of a single failure. Concurrent failures are so common and hidden in complex systems that diagnosis requires not only analysis of failure characteristics, but also correlation between failures. Therefore, in this paper, a concurrent fault diagnosis method is proposed for power equipment based on graph neural networks and knowledge graphs. First, an electrical equipment failure knowledge map is created based on operational and maintenance records to emphasize the relevance of the failed equipment or component. Next, a lightweight graph neural network model is built to detect concurrent faults in the graph data. Finally, a city’s transformer concurrent fault is taken as an example for simulation and validation. Simulation results show that the accuracy and acquisition rate of graph neural network mining in Knowledge Graph is superior to traditional algorithms such as convolutional neural networks, which can achieve the effectiveness and robustness of concurrent fault mining.