## 5 Conclusions  
A total of 175 oil aging samples covering all aging stages of transformer oil-paper insulation were obtained through accelerated thermal aging experiments. The aging states were classified into four and ten categories based on the results of insulation paper aggregation degree tests. A Raman spectroscopy detection platform was used to analyze the insulation oil samples with different aging degrees.  
(1) Wavelet transform and cubic spline smoothing methods were used for denoising and baseline correction to eliminate noise in the raw Raman spectroscopy data, correct baseline drift at different aging stages, and enhance the expression of aging information in the Raman spectroscopy.  
(2) The Isomap algorithm was introduced for feature extraction from the Raman spectroscopy data. The spatial distribution map of the features revealed that the Raman spectroscopy samples of the same aging state were more concentrated, while those of different aging states were more scattered. This feature-extraction method not only greatly reduced the data dimension of the Raman spectroscopy but also effectively extracted and enhanced the discriminative information between different aging states due to the special nature of manifold dimension transformation compared to traditional linear transformations. This was beneficial for improving the accuracy of subsequent discrimination models. Additionally, the formula for determining the value of the nearest-neighbor parameter \(k\) when applying the Isomap feature-extraction method to the discrimination of aging states in oil-paper insulation Raman spectroscopy was studied, and the determined value of \(k\) was validated.  
(3) The entire sample set was divided into training and testing sets in a 7:3 ratio. The BO-XGBoost strong classifier was used to build a discriminative model for oil-paper insulation aging states. Discrimination of the four and ten aging states was performed on samples without feature extraction and samples with feature extraction using the MDS algorithm, PCA algorithm, and Isomap algorithm, respectively. The discrimination results show that the average discriminative accuracy is highest for both the four and ten aging states when the Isomap algorithm is used for feature extraction, reaching 97.0% and 95.1%, respectively. This demonstrates the accurate discrimination of oil-paper insulation aging states using the Isomap feature extraction combined with the BO-XGBoost diagnostic system. The diagnostic model outperforms existing models in terms of category numbers and accuracy and provides a comprehensive method for selecting diagnostic model hyperparameters. It effectively addresses the issue of effective discrimination information being obscured due to redundant Raman spectroscopy data in oil-paper insulation, contributing to the assessment of transformer oil-paper insulation aging states based on Raman spectroscopy.  
Conceptualization, X.C. and Y.F.; methodology, Z.M. and S.T.; validation, N.L., X.S., Y.H., J.Z. and W.Z.; investigation, X.C.; resources, X.C. and Y.F.; data curation, Y.F.; writing--original draft preparation, X.C. and Y.F.; writing--review and editing, Z.M.; visualization, S.T., and N.L.; supervision, X.S. and Y.H.; project administration, J.Z. and W.Z.; funding acquisition, X.C. and Y.F. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China (Grant No. 51977017), the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No. KJZD-K202101103), and Funding results of the Action Plan for the High-quality Development of Postgraduate Education of Chongqing University of Technology (Grant No. gzlcx20233098).  
Not applicable.  
Data Availability Statement: The data presented in this paper are available on request from the corresponding author.  
The authors declare that they have no conflict of interest.