A Novel Condition Assessment Method Based on Dissolved Gas in Transformer Oil

Abstract

In this paper, a condition assessment method based on data driven for power transformer is proposed. Collecting the historical data of dissolved gas content from the same type transformers, the probability density function and cumulative distribution function based on two-parameter Weibull model is established to acquire the distribution law for each gas. The distribution probability between different condition levels is counted to calculate condition threshold by inverse cumulative distribution function, and the condition membership functions based on condition threshold are established. According to the historical data, important weight of each gas is quantified by entropy weight method. Finally, the weighted method is applied to calculate the confidence probability of all the condition levels after obtaining the monitoring data of gas content, and the condition of transformers is determined. The application example shows that the method is data driven without any subjective factor, which assures the accuracy of the results.