Data Restoration of dissolved gas content in transformer oil based on the CS-SVR model

Abstract

Accurate monitoring of the dissolved gas content in transformer oil is crucial for transformers’ safe and stable operation. The early identification for detecting potential power transformer failures is necessary for the stability of an electrical grid. Dissolved gas analysis is an essential technology in transformers diagnosing insulation faults. Missing dissolved gas data can directly impact the reliability of monitoring results of a transformer. This study presents a data plug-in model based on support vector regression (SVR) to restore missing dissolved gas data. To further improve the accuracy of data restoration, the cuckoo search algorithm (CS) is used for optimizing SVR parameters. By verifying H2 and C2H4, the CS-SVR model demonstrates superiority over other plug-in procedures in repairing dissolved gas data.