The diagnosis of abnormal transformer oil temperature is of great significance to guarantee the normal operation of the transformer. Due to concept drift, the oil temperature abnormal diagnosis of the oil-immersed main power transformer is usually unstable via the classic data mining method. Thus, this paper proposes an adaptive abnormal oil temperature diagnosis method (AAOTD) of the transformer based on concept drift. First, the bagging ensemble learning method was used to predict the oil temperature. Then, abnormal diagnosis was performed based on the difference between the predicted oil temperature and the actual measured oil temperature. At the same time, based on the concept drift detection strategy and Adaboost ensemble learning methods, adaptive update of the base classifier in the abnormal diagnosis model was realized. Experiments validated that the algorithm proposed in this paper can significantly reduce the influence of concept drift and has higher oil temperature prediction accuracy. Furthermore, since this method only utilizes the existing power grid data resources to realize abnormal oil temperature diagnosis without extra monitoring equipment, it is an economic and efficient solution for practical scenarios in the electric power industry.
Stator Single-Line-to-Ground Fault Protection for Bus-Connected Powerformers Based on S-Transform and Bagging Ensemble Learning
