Dissolved gas analysis（DGA）technology in the oil is an important means for condition monitoring of power transformer. In view of current mainstream dissolved gases data analysis and diagnosis technology focusing on single point data analysis as well as weak suitability of on line DGA data（high granularity but weak single point precision）

the abnormal identification method of power transformer based on Shapelet is proposed in this paper. The time series decision tree is constructed by the Shapelet discovery algorithm to identify seasonal fluctuation of individual DGA and the typical event in the operation. Coordination with traditional ESD time sequence abnormal detection not only avoids the false alarms of gas production rate and limiting value due to the seasonal fluctuation but also

at the same time

improves greatly the identification sensitivity of typical abnormal events and enhances the interpretation of the algorithm.