Thermal Fault Detection of High-Voltage Isolating Switches Based on Hybrid Data and BERT

The heating of high-voltage isolating switches above 110 kV will cause serious safety accidents. To identify the heating faults of isolating switches accurately, this study uses natural language processing technology, knowledge graph (KG) technology and machine learning algorithm to mine the structured and unstructured mixed data. Firstly, based on bidirectional encoder representations (BERT) pre-training model, a bidirectional long short-term memory network and conditional random field model are introduced to identify entities related to high-voltage isolating switches and the thermal failures from the text data. Further, a convolutional neural network fusion attention mechanism model is leveraged to recognize the corresponding entity relationships from the text records. Then, the KGs of isolating switches and the heating faults are constructed, and a path ranking algorithm is proposed to deduce and identify the possible factors causing overheating on the KGs. Combined with the structured data, a support vector machine model based on focal loss function (FL-SVM) is proposed to recognize the heating faults of isolating switches. Experiments show that the performances of all six models used in this paper on the hybrid dataset are higher than those on the structured dataset. Such results suggest that joint text-structured data mining can compensate for insufficient structured data and thus, achieving a better heating failure recognition, especially for the infrequent malfunctions. Meanwhile, the proposed FL-SVM performs better than other models due to its excellent ability to solve the problem of imbalanced faults and normal samples.