A New DGA Based Transformer Fault Diagnosis Scheme Suitable for Time-Series Fault Data

Abstract

The quality of original data is crucial to the performance of diagnosis model. To improve the performance of transformer diagnosis model based on Dissolved Gas Analysis (DGA), a new diagnosis scheme suitable for time-series dissolved gas data is proposed in this paper. After the analysis of traditional transformer diagnosis architecture, a fault data extraction step is added to the architecture to improve the quality of original fault data. The fault data extraction step is mainly composed of two parts, invalid data correction and determination of possible initial fault time based on fault early warning. Finally, the numerical results validate that the accuracy and sensitivity of DGA based fault diagnosis for the transformer are improved by extracting fault feature of time-series data.