基于拉曼光谱和改进KNN算法的油纸绝缘设备老化阶段判别

Aging Stage Discrimination of Oil-Paper Insulation Equipment Based on Raman Spectrum and Improved KNN Algorithms

电力变压器是电力系统中不可或缺的核心组成。油纸绝缘设备运行老化的过程中,绝缘油或纸在电或热的作用下发生分解产生如糠醛、丙酮、甲醇、CO、CO2等各种反映绝缘老化状态的特征物质,并溶解于油中,使绝缘油中蕴含大量油纸绝缘老化信息。为了对油纸绝缘老化阶段进行有效诊断,本文通过加速热老化试验,获取了大量老化油样,根据老化天数将样本分为12类,并通过拉曼光谱获取了230张拉曼谱图。通过KNN算法,利用皮尔森相关系数对待测样本类别进行了预测,之后对模型引入欧几里得距离,对KNN算法进行了改进,使预测正确率达到了87.92%,并且降低了类别预测偏差。

Power transformer is an indispensable core component of power system.During the aging process of oil-paper insulation equipment,insulation oil or paper decomposes under the action of electricity or heat to produce various characteristic substances reflecting the aging state of insulation,such as furfural,acetone,methanol,CO,CO2,and dissolves in oil,which contains a lot of aging information of oil-paper insulation.In order to diagnose the aging stage of oil-paper insulation effectively,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 to predict the class of test samples by Pearson correlation coefficient.Then the Euclidean distance is introduced into the model,and the KNN algorithm is improved.The prediction accuracy reaches 87.92%,and the class prediction bias is reduced.