Intelligent fault diagnosis and operation condition monitoring of transformer based on multi-source data fusion and mining

Transformers are important equipment in the power system and their reliable and safe operation is an important guarantee for the high-efficiency operation of the power system. In order to achieve the prognostics and health management of the transformer, a novel intelligent fault diagnosis of the transformer based on multi-source data fusion and correlation analysis is proposed. Firstly, data fusion for multiple components of transformer dissolved gases is performed by an improved entropy weighting method. Then, the combination of bidirectional long short-term memory network, attention mechanism, and convolution neural network is employed to predict the load rate, upper oil temperature, winding temperature data, and the fusion indices of dissolved gas components in the transformer. Furthermore, Apriori correlation analysis is performed on the transformer load rate and upper oil layer, winding temperature, and fusion indices of gas components by support and confidence levels to achieve a predictive assessment of the transformer state. Finally, the validity of the algorithm is verified by applying actual data from a power system monitoring platform. The results show that in the vicinity of sample point 88, the dissolved gas, upper oil temperature, and winding temperature data are not within the normal range of intervals, and it is presumed that the arc discharge phenomenon. Furthermore, the average correct fault diagnosis rate of 100 diagnoses of the transformer fault diagnosis model proposed in this paper is 0.917, and the mean square error of the correct rate is 0.018. The proposed model can achieve the prediction of the accident early warning, to prevent further expansion of the accident.