Fault Prediction using a Grey-Markov Model from the Dissolved Gases Contents in Transformer Oils

Abstract

A novel method to predict transformer fault by forecasting the variation trend of the dissolved gases content is proposed. After the content of each feature gas, such as hydrogen and methane, is obtained by the proposed forecasting model, the fault type can be diagnosed by the dissolved gas analysis (DGA) technologies. Firstly, the GM (1,1) grey model with unequal time interval is introduced to generate a general forecasting model for each feature gas. The introduced grey model with unequal time interval will enforce no constrain on the historical measurement data. Consequently, the time intervals of the two adjacent measuring points can be either constant or variant. To address the deficiency that the existing grey model is unable to describe the fluctuation of the predicted object in time domain, the Markov chain is introduced to improve the accuracy of the grey forecasting model. An adaptive method to automatically divide the state space based on the number of states and the relative error of the grey model is presented by using Fibonacci sequences. Practical measurements are used to verify the accuracy of the proposed forecasting model. The numerical results show that there is high probability (86%) that the proposed grey-Markov model acquires a smaller prediction residual as compared to the original GM(1,1) grey model.