Research on Intelligent Diagnosis Method of Oil Temperature Defect in Distribution Transformer Based on Machine Learning

Abstract

Long-time operation under abnormal oil temperature is one of the critical factors causing short service life and limit capacity of distribution transformers, and even tripping accident due to insulator failure. At present, only a simple static alarm function is available to detect the over-limit of oil temperature in distribution transformer. This function cannot disclose the defect evolvement. A prior alarm function to early identify abnormal oil temperature is needed in order to stop the development of defects. This paper introduces a novel intelligent prediction method based on machine learning methods for real-time abnormal oil temperature detection. This method establishes a dynamic association learning model using decision forests algorithm to predict oil temperatures based on transformer parameter, power load, as well as weather condition under the normal operation state. Comparing the predicted oil temperature with online measurement, we can find the abnormal oil temperature state and develop prior alarm function to detect the defect of distribution transformer. The decision making procedure based on this method has been applied into distribution transformers of Shanghai. The results validate the accuracy of the method and show efficiency when applying on the maintenance planning of distribution transformer.