# 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
Figure 4: Tendency of abnormal oil temperature warnings from various methods. The 4 subgraphs (**a–d**) show the results of six algorithms for oil temperature prediction of the 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 early warning rate of the transformer. However, due to the ensemble learning steps, the computational complexity of the algorithm proposed in this paper is higher than the traditional methods. Therefore, in the future work of this research, we will focus on reducing the computational complexity of the algorithm. Conceptualization, Y.Z. and Z.Z. ; methodology, Y.Z., Z.Z., J.X. and R.H.; software, Z.Z., R.H. and J.X. ; validation, Y.Z. and J.X. ; formal analysis, Y.Z. and Z.Z. ; investigation, Y.Z., J.X. and R.H.; resources, J.X. and R.H.; data curation, Z.Z. and J.X. ; writing--original draft preparation, Y.Z. and Z.Z. ; writing--review and editing, Y.Z., J.X. and R.H.; visualization, Z.Z. and Y.Z. ; supervision, J.X. and R.H.; project administration, J.X. and R.H.; funding acquisition, J.X. and R.H. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China, grant number 61763031, and Jiangxi Provincial Natural Science Foundation, grant number 20202 BAB202018. Not applicable. We are grateful for the National Natural Science Foundation of China and the Jiangxi Provincial Natural Science Foundation. The authors declare no conflict of interest. ## References

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