With the development of the smart grid and energy Internet, the power industry generates huge, multi-source, heterogeneous, and highly coupled data, which are difficult to utilize. The intelligent operation and maintenance system of the power transformer based on the knowledge graph and graph neural network is developed in this article. The multi-source heterogeneous data are structured and modeled by the constructed knowledge graph, and it presents the correlation among data more intuitively. On this basis, the graph neural network is designed to achieve the prediction and excavate the deep information hidden in the data. The testing results show that the system has fully used the multi-dimensional and interrelated heterogeneous data, achieving a deep information mine. It benefits the management and strategy implementation for the system scientifically and guides the operation and maintenance of the transformer. The system is of great significance on improving the efficiency of the transformer maintenance and safe operation.

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This article presents a triple extraction technique for a power transformer fault information process based on a residual dilate gated convolution and self-attention mechanism. An optimized word input sequence is designed to improve the effectiveness of triple extraction. A residual dilate gated convolution is used to capture the middle-long distance information in the literature. A self-attention mechanism is applied to learn the internal information and capture the internal structure of input sequences. An improved binary tagging method with position information is presented to mark the start and the end of an entity, which improves the extraction accuracy. An object entity is obtained by a specific relationship r for a given subject. The nearest start-end pair matching the principle and probability estimation is applied to acquire the optimal solution of the set of triples. Testing results showed that the F1 score of the presented method is 91.98%, and the triple extraction accuracy is much better than the methods of BERT and Bi-LSTM-CRF.