# Adaptive Abnormal Oil Temperature Diagnosis Method of Transformer Based on Concept Drift
## 1 Introduction
However, the forgetting mechanism of a single classifier still has many problems, such as difficulty in window selection, nonconvergence, unstable learning, and unsuitability for the detection of gradual concept drift. Therefore, many researchers have constructed multiple simple classifiers, namely ensemble learning, to overcome the problem of data concept drift. [PERSON] et al. [17] proposed an on-line weighted ensemble (OWE) method based on a regression model which can retain the old concept information when the concept recurs. The AWE [18] algorithm is improved on the basis of algorithm SEA, which sets weights according to the classification accuracy of each base classifier and directly replaces the base classifier with the smallest weight when updating the classifier. The CDOL algorithm [19] consists of two weighted base classifiers. After detecting the concept drift, the new base classifier is trained with new samples, and the one with the smaller weight in the original base classifier is removed. [PERSON] et al. [20] proposed a new ensemble model, namely dynamic clustering forest (DCF), which is used for the concept drift of text stream classification. However, the adaptive ensemble learning method only evaluates which base classifiers to delete based on the classification accuracy of each base classifier for the current data block, ignoring the historical importance of the base classifier. In addition, a small concept may cause historical importance base classifiers to be deleted by mistake, causing the retained base classifier to not be globally dominant. Thus, it is difficult to obtain good prediction results [21]. One of the mainstream ideas for detecting concept drift is to judge whether the drift has occurred based on the change trend of the classification model test accuracy [22; 23]. This idea needs to ensure that the classification model decides on the new sample before it proceeds to the next training. The detector analyzes the change trend of classification accuracy in time [24]. Aiming at the problem of concept drift in the abnormal diagnosis of transformer oil temperature, this paper proposes an adaptive oil temperature abnormal diagnosis method based on concept drift. The main work is as follows:  
1. The diagnosis method of transformer abnormality is proposed. First, a series of base classifiers are generated in parallel using datasets randomly selected from a dataset of transformer historical operating conditions. Then an integrated decision is made with the help of a voting weighting mechanism to predict the oil temperature of the transformer. Finally, the abnormal diagnosis of the transformer is performed based on the relationship between the predicted oil temperature, the measured oil temperature and the time of the three judgment thresholds. 2. A concept drift detection method based on three-branch decision and an adaptive update algorithm of classifier based on ensemble learning are proposed to judge which decision should be made through the test accuracy of the model. If the decision model is no longer applicable to the data at this stage, the base learners and weights are adjusted according to the learning effect of each base learner on the previous data. In this way, the poorly performing base learners in the model are continuously replaced to ensure that each base classifier in the model is optimized. The model converge quickly and achieves stable learning results, and the historical importance of the base classifier is not ignored. The model can be adapted to the concept drift data stream. This paper is organized as follows. After the introduction in Section 1, Section 2 illustrates the dataset in this paper and explains the methodology of adaptive abnormal oil temperature diagnosis method (AAOTD). Section 3 analyzes and discusses the experimental results to validate the efficiency of AAOTD method. Section 4 summarizes the work of this paper and represents our perspective.