A Novel Deep Recurrent Belief Network Model for Trend Prediction of Transformer DGA Data

Abstract

Oil chromatography data together with its variation trend provide the key basis for the evaluation of the transformer health state. The existing studies on deep belief network (DBN) and support vector machine (SVM) have reported a few results in the field of oil chromatography data prediction. However, the above-mentioned methods are proposed for the classification problem, so there is no theoretical basis for applying the above-mentioned methods to the time-series prediction. The wrong usage limits the accuracy of the predicted results, which is observed as the obvious “time-shift” error in the prediction curve, leading to the predicted state inconsistent with the actual situation. To fill the gap, a deep recurrent belief network (DRBN) model for the transformer state prediction was proposed based on the time-series theory and oil chromatography data characteristics. In this model, the construction shortage of DBN in time-series prediction was analyzed as a prototype pattern to structure the stereoscopic mapping relation. The self-adaptive delay network based on the principle of autocorrelation, together with the corresponding error feedback network, realized the stereoscopic flow of data in multi-dimensional space and, thus, ensured the model's valid expression of the time-domain correlation. In addition, the cross-entropy loss function, based on the Kullback-Leibler divergence and the Weibull distribution with an obvious characteristic of DGA, was constructed to eliminate the uncertainty of initialization process and also effectively control the direction and step size of the error gradient. The examples in the field were used to verify the method to find that the model proposed in this paper can availably overcome the “time-shift” error, and its prediction accuracy can reach more than 95.16%.