Aging Stage Diagnosis of Oil-Paper Insulation Equipment Using Raman Spectrum Based on Multiple screening KNN Algorithms

2021 International Conference on Electrical Materials and Power Equipment (ICEMPE) Pub Date : 2021-04-11 DOI:10.1109/ICEMPE51623.2021.9509179

Yongkuo Zhou, Weigen Chen, Dingkun Yang, Ruyue Zhang

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Abstract

Rapid identification of aging state of oil-paper insulation is of great significance to the operation safety of power transformers. Raman spectroscopy can rapidly analyze the aging characteristic information dissolved in oil, and it is an effective means for the aging diagnosis of oil-paper insulation. In this paper, Multiple screening KNN Algorithms for Raman spectroscopy analysis of aging oil-paper insulation samples is presented. A large number of aging oil samples were obtained by accelerated thermal aging test. According to the aging days, the samples were divided into 12 categories, and 230 Raman spectra were obtained by Raman spectroscopy. The KNN algorithm is used for classification and regression of Raman Spectra of test samples by Pearson correlation coefficient. Then, based on the traditional KNN algorithm, a multi-screening KNN is proposed according to the actual situation of the aging process of insulating oil Raman spectrum. The prediction of Multiple screening KNN Algorithms accuracy of classification reaches 87.92%, and the RMSE of regression reached 54.28.

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基于多重筛选KNN算法的拉曼光谱油纸绝缘设备老化阶段诊断

油纸绝缘老化状态的快速识别对电力变压器的运行安全具有重要意义。拉曼光谱可以快速分析油中溶解的老化特征信息，是油纸绝缘老化诊断的有效手段。提出了用于老化油纸绝缘样品拉曼光谱分析的多重筛选KNN算法。通过加速热老化试验，获得了大量的老化油样。根据老化天数将样品分为12类，利用拉曼光谱法获得230张拉曼光谱。采用KNN算法，通过Pearson相关系数对测试样本的拉曼光谱进行分类和回归。然后，在传统KNN算法的基础上，根据绝缘油拉曼光谱老化过程的实际情况，提出了一种多筛选KNN算法。多重筛选KNN算法的分类预测准确率达到87.92%，回归的RMSE达到54.28。

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2021 International Conference on Electrical Materials and Power Equipment (ICEMPE)

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