Prediction of dissolved gas concentration in transformer oil considering data loss scenarios in power system

Abstract

The trend prediction of dissolved gas concentration in transformer oil can provide basis for transformer fault diagnosis, which is of vital significance to the safe operation of power system. However, due to the inevitable malfunction of monitoring equipment, it is difficult to collect all needed data in actual operation scenarios. Therefore, a method for predicting dissolved gas concentration in transformer oil for data loss scenarios is proposed based on Bayesian probabilistic matrix factorization (BPMF) and gated recurrent unit (GRU) neural network. Firstly, aiming at the problem of data loss in actual monitoring of dissolved gas in oil, BPMF is used to fill in the missing data. Then, a GRU neural network model is established to predict the trend of dissolved gas concentration in oil. Finally, the hyperparameters of the prediction model are selected and optimized by Bayesian theory. The examples show that this method can effectively fill in the missing part of the measured data. Compared with traditional prediction methods, the proposed method has higher prediction accuracy.