A prediction model for dissolved gas in transformer oil based on improved verhulst grey theory
Power transformer is one of the most expensive component of electrical power plants and the failures of such transformer can result in serious power system issues, so fault forecasting for power transformer is very important to insure the whole power system runs normally. In this paper, a new improved non-equal-gap verhulst grey prediction model for dissolved gases in power transformer was developed. The proposed approach has been verified by the non-equal-gap fault dissolved gas analysis data of a power transformer in Shenhai electric factory, and the experimental results show the proposed model has obvious advantages and has comparatively higher prediction accuracy than the traditional grey prediction model.