# 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
This shows that the performance of the AAOTD algorithm is stronger than other algorithms used in this experiment. Next, we input the predicted oil temperature values of the above six algorithms into Algorithm 2 (Abnormal detection of transformer's working state) and calculated the changes in the warning rate of each algorithm in each prediction set according to Formula 8 to obtain Figure 4.

The four subgraphs (a), (b), (c), and (d) of Figure 4 plot the warning rate changes curves of the transformers in Xinyu, Gulu, Xuzhou, and Yanghang of the six algorithms. The yellow curve represents the abnormal rate of the transformer under real conditions. In real situations, there were fewer cases of warning and error in the transformer. The broken line of the AAOTD and AWE algorithm is more consistent with the broken line in the real situation, among which the AAOTD algorithm is the best. It can be seen from Figure 4 that the other four algorithms also performed poorly in the early warning of the transformers. This is because they do not consider concept drift, so the accuracy of oil temperature prediction was not high, which led to poor diagnosis results of abnormal transformers. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**Initial Accuracy**} & \multicolumn{3}{c}{**Accuracy Rate after the Concept Drift**} & \multirow{2}{*}{**Final Accuracy**} \\ \cline{3-3} \cline{5-5}  & & **First Time** & & **Second Time** & **Third Time** \\ \hline BAOTD & 0.9954 & \(-\)0.2863 & \(-\)0.1963 & \(-\)0.8266 & 0.3376 \\ CART & 0.9743 & \(-\)0.5079 & \(-\)0.1409 & \(-\)0.1107 & 0.0027 \\ RF & 0.9845 & \(-\)0.2827 & \(-\)0.2008 & \(-\)0.8397 & 0.3439 \\ SVM & 0.9896 & \(-\)0.3228 & \(-\)0.2498 & \(-\)0.7627 & 0.1895 \\ AWE & 0.9898 & \(-\)0.0108 & +0.0235 & +0.1217 & 0.8515 \\ AAOTD & 0.9942 & +0.0012 & +0.0598 & +0.1538 & 0.9555 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The results of the 6 algorithms on the transformer in the Xinyu area: The initial accuracy, the change of accuracy in the 3 conceptual drifts (a positive number represents an increase in accuracy, a negative number represents a decrease in accuracy), and the final accuracy. Overall, the AAOTD algorithm proposed in this paper had a higher oil temperature prediction accuracy and more correct oil temperature abnormal warning rate in the oil-immersed transformer-related dataset. Compared with classic concept drift adaptation algorithms and other machine learning algorithms that do not consider that the transformer oil temperature working condition data is prone to concept drift, this algorithm has concept drift adaptation capability, better transformer abnormal diagnosis results, and is more robust. ## 4 Conclusions

In this study, an adaptive abnormal oil temperature diagnosis (AAOTD) method based on the concept drift was proposed. This method can significantly reduce the influence of concept drift from performance degradation of the transformer's components. The contributions of this paper are as follows. First, a new concept drift detection model was developed using three-way decision-making theories, which can infer the influence level of concept drift based on changes of model accuracy. Then, ensemble learning algorithm was employed to adaptively adjust the base learners and their weights to reduce the influence of concept drift. The efficiency of AAOTD methods was validated via the experiments. In the experiments, the average oil temperature prediction accuracy of AAOTD in four regions reached 95.03% (Xinyu: 94.27%, Gulu: 93.58%, Xuzhou: 95.82%, Yanghang: 94.46%), which is 1.52% higher than the AWE algorithm and 20-40% higher than other traditional machine learning algorithms. 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.