# Adaptive Abnormal Oil Temperature Diagnosis Method of Transformer Based on Concept Drift
## 1 Introduction  
The transformer is a core piece of equipment in the power system [1]. If the transformer is overloaded for a long time, then the oil temperature of the transformer will be conspicuously higher than the normal standard, the components of the transformer will experience accelerated aging, and the working condition of the transformer will be unstable [2]. Therefore, the abnormal diagnosis of transformer's oil temperature is a significant issue to maintain the stable and safe operation of the transformer. In recent decades, there has been important progress in the field of transformer oil temperature abnormal diagnosis. However, traditional transformer oil temperature abnormal diagnosis is generally based on the thermal circuit model and oil chromatographic analysis [3]. Based on the monitoring and analysis of related parameters from the chromatographic method, it can be judged whether the top-level oil temperature of the transformer is abnormal or not. Although the accuracy of the chromatographic method is high, it still has many problems, such as complicated parameter adjustment, the expensive cost of deployment, equipment maintenance, etc. Those problems make it difficult to widely use this method in most practical scenarios. With the development of artificial intelligence research, scholars have attempted to use machine learning technology to analyze historical working condition data of the transformer to diagnose the abnormal oil temperature of the transformer. [PERSON] et al. [4] proposed a method based on the artificial neural network to predict the top-level oil temperature of the transformer. [PERSON] et al. [5] proposed a method based on the ANN algorithm to predict the internal oil temperature of the transformer. [PERSON] et al. [6] appliedthe least squares support vector machine, integrated with the adaptive boost algorithm, to the transformer fault diagnosis. However, [PERSON] et al. [3] found that the method based on machine learning is more convenient and easier to realize, but the accuracy of this type of diagnosis method is generally not high. However, based on our research, we found that the reason leading to the low accuracy is always related to unpredictable fluctuations of transformer working condition data, which are caused by the continuous oxidation of the internal components of the transformer and the irregular fluctuation of the power load of the transformer [7]. It can be considered as a typical concept drift phenomenon. Concept drift means that the performance of the previously trained classification model or regression model may experience irregular and sharp deterioration when the probabilistic distribution of data changes over time [8]. Since concept drift was proposed by [PERSON] in 1986 [9], it has remained a hot research topic in the field of machine learning. To date, several researchers have obtained results regarding the problem of data concept drift. The existing processing methods can be roughly divided into incremental learning [10] and ensemble learning [11; 12]. The incremental learning method uses a single classifier to learn from data and dynamically adjusts the model to achieve the characteristics of adapting to data changes. For example, the concept drift detection method based on a sliding window obtains new samples by continuously sliding the window forward for incremental learning and performs comparisons with the old distribution data to detect concept drift [13; 14]. A common method is the concept drift detection method based on a single window (SWCDS), which detects the changes of data distribution by periodically detecting the change of the classification error rate in the sliding window [13]. [PERSON] et al. [15] proposed a window adjustment method based on information entropy to determine the distribution of new and old instances of the window (ADDM) in 2014. [PERSON] et al. [16] proposed an accelerated Hoeffding drift detection method (FHDDM).