# Adaptive Abnormal Oil Temperature Diagnosis Method of Transformer Based on Concept Drift
## 2 Materials and Methods
### Concept Drift Solution of Oil Temperature Prediction and Abnormal Detection Model
However, these two algorithms (AAOTD and AWE) can update themselves with the continuous arrival of new data, which is why the prediction accuracy of these two algorithms is better than the other four algorithms. Both the AAOTD and AWE algorithms use an ensemble framework to build the algorithm model and use the regression tree model as the base classifier in the ensemble algorithm. The relevant parameter settings of the base classifier are also the same. However, it can be seen from the comparison of the red and brown lines in Figure 3 that the AAOTD algorithm had a higher prediction accuracy than the AWE algorithm. This is because the AWE algorithm only replaces the base classifier by judging whether the performance of the base classifier is lower than the minimum threshold. This update method ignores the historical importance of the base classifier, and a small conceptual fluctuation may cause historically important base classifiers to be deleted by mistake. The method proposed in this paper to detect concept drift based on the three-way decision-making rule is a decision model based on human cognition, which can effectively avoid the influence of false concept fluctuations caused by misjudgment. Moreover, in updating the base classifier, the AAOTD algorithm adopts the update strategy of Adaboost, which makes it possible to avoid ensemble learning to overcome the problem of concept drift in the data. Given working condition of transformer in Xinyu, Table 3 lists the detail of various methods' evaluations. Figure 3: Chart of oil temperature prediction accuracy rate from various algorithms. The 4 subgraphs (**a–d**) show the results of 6 algorithms for oil temperature prediction of transformers in 4 different regions (Xinyu, Gulu, Xuzhou, and Yanghang). The x-coordinate represents the serial number of the prediction set, and the y-coordinate represents the accuracy of the predicted oil temperature. The AWE, AAOTD, and BAOTD algorithms had high accuracy in predicting oil temperature, reaching 98.98%, 99.42%, and 99.54% accuracy, respectively. This is because these three algorithms use an ensemble algorithm model. The final prediction result of the ensemble algorithm was obtained by the weighted average of the oil temperature prediction results of each base learner in the algorithm. Compared with a single complex classifier, this ensemble method has more stable accuracy, which also shows that using integrated algorithms is better in the context of transformer oil temperature prediction. Table 3 shows the three significant declines in the accuracy of oil temperature prediction. Among them, the accuracy of the BAOTD, CART, RF, and SVM algorithms decreased because these algorithms themselves do not have the ability to adapt to concept drift. When the concept drift occurred in the oil temperature working condition data, the abovementioned algorithms (BALOTD, CART, RF, and SVM) did not use the latest oil temperature working condition data to update the algorithm model, but still used the algorithm model trained with historical oil temperature operating condition data to predict the oil temperature. So, with the continuous change of the concept drift in the oil temperature working condition data, the accuracy of the algorithm model will continue to decline, while the accuracy of the algorithms with self-renewal (AWE and AAOTD) will experience a smaller decline. For the AAOTD algorithm, when the accuracy of the algorithm changes, the concept drift detection will be performed according to the Concept Drift Detection Algorithm mentioned in Section 2. Then, different decisions will be made according to the Adaptive Update Algorithm of Base Classifiers Based on Adaboost Ensemble Learning algorithm to update the base classifiers, so that the updated base classifier can adapt to the conceptual drift in the oil temperature working condition data. From the data in the table, the final accuracy rate of the oil temperature prediction of the AAOTD algorithm proposed in this paper only slightly decreased, still reaching 95.55%.