作者:赵京鹤，修大元，王金龙，池明赫
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Authors:ZHAO Jing-he,XIU Da-yuan,WANG Jin-long,CHI Ming-he
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摘要:摘要:及时发现变压器中局部放电从而避免故障，是提高变压器质量保障电网安全运行的重要手段。为快速识别变压器绝缘纸板缺陷类型以便排除故障，在实验室中模拟变压器绝缘纸板中常见的三种绝缘缺陷，并进行了局部放电试验，结果表明，三种试样的局部放电分别具有独特的特征。无缺陷纸板试样的局部放电多在0°~120°、180°~300°的相位上发生；气隙缺陷试样则在60°~160°、240°~330°相位上发生放电；金属颗粒缺陷试样则在80°~160°、260°~340°间呈脉冲状放电。在此基础上，针对部分局部放电实验仪只能导出图片数据需要二次提取文本数据对分析带来不便的问题，基于实验所得PRPD（phase resolved partial discharge）图构建数据集，并以其80%作为训练集、20%作为测试集采用卷积神经网络（convolutional neural networks，CNN）与K-邻近法 （K-nearestneighbors，KNN）、支持向量机(support vector machine，SVM)及BP神经网络的识别结果进行对比。所构建并优化的卷积神经网络，在训练集与测试集上分别取得了约96.5%、89.9%的正确率，高于传统识别方法。成功将卷积神经网络用于以PRPD图像为基础的局部放电识别。
Abstract:Abstract: Finding out the partial discharge in time is an important means to avoid fault， improving the quality of transformer and ensuring safe operation of power grid. In order to identify defect type of transformer insulation pressboard quickly for removal of fault， the partial discharge experiments of the three kinds of insulation defects common in transformer insulation pressboard was carried out in laboratory. Results show that the partial discharge of the three samples has unique characteristics. The partial discharge of flawless pressboard specimens occurs mostly at the phases of 0 to 120 degrees and 180 degrees to 300 degrees， while the air gap defect samples are discharged at the phase of 60 to 160 degrees， 240 degrees to 330 degrees， and the metal particle defect samples are pulsed at 80 to 160 degrees and 260 degrees to 340 degrees. On certain condition， it is need to extracted the text data secondarily， for some partial discharge experimenter can only export picture data. Based on the experimental PRPD（Phase Resolved Partial Discharge） graph， a data set was built， with 80% as a training set， 20% as a test set. A convolutional neural networks (CNN) was adopted to analyse the data set， compared with K-Nearest Neighbors(KNN)， Support Vector Machine(SVM) and Error Back Propagation Training. The convolutional neural network， which is constructed and optimized， obtains the correct rate of about 96.5% and 89.9% respectively in the training set and the test set,
\nshowing that convolutional neural networks are suitable for local discharge recognition based on PRPD images.


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