灰度纹理与油气特征融合的油纸绝缘老化状态评估

doi:10.11930/j.issn.1004-9649.202004095

中国电力 ›› 2020, Vol. 53 ›› Issue (12): 159-166,197.DOI: 10.11930/j.issn.1004-9649.202004095

灰度纹理与油气特征融合的油纸绝缘老化状态评估

刘东超¹, 林语¹, 原辉², 王帅², 姜敏², 俞华², 李海涛¹

1. 南京南瑞继保电气有限公司，江苏南京 211102;
2. 国网山西省电力公司电力科学研究院，山西太原 030001

收稿日期:2020-04-13 修回日期:2020-05-13 发布日期:2020-12-16
作者简介:刘东超(1980—)，男，高级工程师，从事智能一次设备研究，Email：liudc@nrec.com
基金资助:
国家电网有限公司科技项目(第三代智能变电站辅助系统研究，520530180015)

Aging State Evaluation of Oil-Paper Insulation Based on Fusion of Gray Texture and Oil Gas Features

LIU Dongchao¹, LIN Yu¹, YUAN Hui², WANG Shuai², JIANG Min², YU Hua², LI Haitao¹

1. NR Electric Co., Ltd., Nanjing 211102, China;
2. State Grid Shanxi Electric Power Company Electric Power Research Institute, Taiyuan 030001, China

Received:2020-04-13 Revised:2020-05-13 Published:2020-12-16
Supported by:
This work is supported by Science and Technology Project of SGCC (Research on the Third-Generation Intelligent Substation Auxiliary System, No.520530180015)

摘要/Abstract

摘要： 针对单一局部放电特征辨析老化信息量存在精度不足的问题，提出一种基于局部放电灰度纹理特征与油气特征的D-S证据融合识别方法，对油纸绝缘老化状态进行分析。首先，通过试验构造人工油纸绝缘内部气隙模型，模拟变压器内部实际运行环境，采集油纸绝缘7个老化层级的局部放电信号及油中气体含量，提取各老化层级的灰度纹理特征、统计特征及油气特征，利用支持向量机进行识别。将灰度纹理特征和油气特征的识别结果输入到D-S证据融合框架中进一步识别分析，识别结果与单一特征及其他两两特征融合结果比较。试验结果表明：基于灰度纹理特征与油气特征的D-S证据融合方法对油纸绝缘老化状态的识别效果更佳。

关键词: 局部放电, 灰度纹理特征, 油气特征, D-S证据融合

Abstract: Aiming at the problem of insufficient precision of aging information obtained by single partial discharge(PD) feature discrimination, a D-S evidence fusion recognition method based on PD gray texture and oil-gas features is proposed to analyze the aging state of oil-paper insulation. Firstly, an air gap model of artificial oil-paper insulation is constructed through experiments to simulate the actual operation environment inside the transformer. Next, the PD signals of seven aging levels of oil-paper insulation and the corresponding gas content in oil are collected. Then, the gray texture features, statistical features and oil-gas features of each aging level are extracted, and subsequently identified using the support vector machine. The identification results of gray texture features and oil-gas features are then input into the D-S evidence fusion framework for further recognition and analysis. Finally, the recognition results are compared with the single feature and other two-feature fusion results. The experimental results show that the D-S evidence fusion method based on gray texture feature and oil-gas feature can better identify the aging state of oil-paper insulation.

Key words: partial discharge, gray texture feature, oil-gas feature, D-S evidence fusion

刘东超, 林语, 原辉, 王帅, 姜敏, 俞华, 李海涛. 灰度纹理与油气特征融合的油纸绝缘老化状态评估[J]. 中国电力, 2020, 53(12): 159-166,197.

LIU Dongchao, LIN Yu, YUAN Hui, WANG Shuai, JIANG Min, YU Hua, LI Haitao. Aging State Evaluation of Oil-Paper Insulation Based on Fusion of Gray Texture and Oil Gas Features[J]. Electric Power, 2020, 53(12): 159-166,197.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202004095

https://www.electricpower.com.cn/CN/Y2020/V53/I12/159

参考文献

[1] 李予全, 寇晓适, 郑含博, 等. 有载分接开关油箱渗漏导致变压器油色谱数据异常诊断[J]. 中国电力, 2017, 50(3): 133-136
LI Yuquan, KOU Xiaoshi, ZHENG Hanbo, et al. Diagnosis and treatment of abnormal chromatographic transformer oil caused by on-load tap changer oil leakage[J]. Electric Power, 2017, 50(3): 133-136
[2] HAO J, LIAO R J, CHEN G, et al. Quantitative analysis ageing status of natural ester-paper insulation and mineral oil-paper insulation by polarization/depolarization current[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2012, 19(1): 188-199.
[3] 杨丽君, 廖瑞金, 孙会刚, 等. 变压器油纸绝缘热老化特性及特征量研究[J]. 电工技术学报, 2009, 24(8): 27-33
YANG Lijun, LIAO Ruijin, SUN Huigang, et al. Investigation on properties and characteristics of oil-paper insulation in transformer during thermal degradation process[J]. Transactions of China Electrotechnical Society, 2009, 24(8): 27-33
[4] MEHDIZADEH S, YAZDCHI M, NIROOMAND M. A novel AE based algorithm for PD localization in power transformers[J]. Journal of Electrical Engineering and Technology, 2013, 8(6): 1487-1496.
[5] 郑含博. 电力变压器状态评估及故障诊断方法研究[D]. 重庆: 重庆大学, 2012.
ZHENG Hanbo. Study on condition assessment and fault diagnosis approaches for power transformers[D]. Chongqing: Chongqing University, 2012.
[6] 律方成, 金虎, 王子建, 等. 基于主成分分析和多分类相关向量机的GIS局部放电模式识别[J]. 电工技术学报, 2015, 30(6): 225-231
LV Fangcheng, JIN Hu, WANG Zijian, et al. GIS partial discharge pattern recognition based on principal component analysis and milticlass relevance vector machine[J]. Transactions of China Electrotechnical Society, 2015, 30(6): 225-231
[7] LIAO R J, YANG L J, LI J, et al. Aging condition assessment of transformer oil-paper insulation model based on partial discharge analysis[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2011, 18(1): 303-311.
[8] 王国利, 郝艳捧, 贾志东, 等. 电力变压器典型局放模型放电脉冲的特性研究[J]. 高电压技术, 2001, 27(2): 5-8
WANG Guoli, HAO Yanpeng, JIA Zhidong, et al. Study on pulse current of typical PD models in power transformer[J]. High Voltage Engineering, 2001, 27(2): 5-8
[9] 周沙, 景亮. 基于矩特征与概率神经网络的局部放电模式识别[J]. 电力系统保护与控制, 2016, 44(3): 98-102
ZHOU Sha, JING Liang. Pattern recognition of partial discharge based on moment features and probabilistic neural network[J]. Power System Protection and Control, 2016, 44(3): 98-102
[10] 弓艳朋, 刘有为, 吴立远. 采用分形和支持向量机的气体绝缘组合电器局部放电类型识别[J]. 电网技术, 2011, 35(3): 135-139
GONG Yanpeng, LIU Youwei, WU Liyuan. Identification of partial discharge in gas insulated switchgears with fractal theory and support vector machine[J]. Power System Technology, 2011, 35(3): 135-139
[11] 徐艳春, 夏海廷, 李振华, 等. 采用动态模式分解分形特征的局部放电模式识别[J]. 电力系统及其自动化学报, 2019, 31(12): 35-43
XU Yanchun, XIA Haiting, LI Zhenhua, et al. Partial discharge pattern recognition based on fractal features using dynamic mode decomposition[J]. Proceedings of the CSU-EPSA, 2019, 31(12): 35-43
[12] ROSTAMINIA R, SANIEI M, PARVIN V, et al. Accurate power transformer PD pattern recognition via its model[J]. IET Science, Measurement & Technology, 2016, 10(7): 745-753.
[13] 魏振, 齐波, 左健, 等. 基于局部放电图像特征的换流变压器油纸绝缘缺陷诊断方法[J]. 电网技术, 2015, 39(4): 1160-1166
WEI Zhen, QI Bo, ZUO Jian, et al. A method to diagnose defects in oil-paper insulation of converter transformer based on image feature of partial discharge[J]. Power System Technology, 2015, 39(4): 1160-1166
[14] 褚鑫, 张建文, 韩刚. 统计特征参数及多分类SVM的局部放电类型识别[J]. 电测与仪表, 2015, 52(7): 35-39, 96
CHU Xin, ZHANG Jianwen, HAN Gang. Partial discharge pattern recognition based on statistical parameters and multi-classifications SVM[J]. Electrical Measurement & Instrumentation, 2015, 52(7): 35-39, 96
[15] 解浩, 唐建伟, 严家明, 等. 热老化过程中油浸绝缘纸局部放电能量特性分析[J]. 绝缘材料, 2018, 51(4): 69-74
XIE Hao, TANG Jianwei, YAN Jiaming, et al. Analysis of partial discharge energy characteristics of oil-immersed insulating paper during thermal ageing process[J]. Insulating Materials, 2018, 51(4): 69-74
[16] 黄亮, 唐炬, 凌超, 等. 基于多特征信息融合技术的局部放电模式识别研究[J]. 高电压技术, 2015, 41(3): 947-955
HUANG Liang, TANG Ju, LING Chao, et al. Pattern recognition for partial discharge based on multi-feature fusion technology[J]. High Voltage Engineering, 2015, 41(3): 947-955
[17] SUGANYA R, RAJARAM S. Feature extraction and classification of ultrasound liver images using haralick texture-primitive features: Application of SVM classifier[C]//2013 International Conference on Recent Trends in Information Technology (ICRTIT), July 25-27, 2013. Chennai, India. IEEE, 2013: 596-602
[18] 张建文. 电气设备故障诊断技术[M]. 北京: 中国水利水电出版社, 2006.
[19] 唐炬, 卓然, 吴冕之, 等. 气体绝缘电器局部放电模式的多特征信息融合决策诊断[J]. 高电压技术, 2013, 39(11): 2581-2588
TANG Ju, ZHUO Ran, WU Mianzhi, et al. Multiple-characteristics information-fusion decision diagnosis of partial discharge pattern of gas insulated switchgear[J]. High Voltage Engineering, 2013, 39(11): 2581-2588
[20] 吐松江·卡日, 高文胜, 张紫薇, 等. 基于支持向量机和遗传算法的变压器故障诊断[J]. 清华大学学报(自然科学版), 2018, 58(7): 623-629
KARI·Tusongjiang, GAO Wensheng, ZHANG Ziwei, et al. Power transformer fault diagnosis based on a support vector machine and a genetic algorithm[J]. Journal of Tsinghua University (Science&Technology), 2018, 58(7): 623-629

灰度纹理与油气特征融合的油纸绝缘老化状态评估

Aging State Evaluation of Oil-Paper Insulation Based on Fusion of Gray Texture and Oil Gas Features

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	鞠玲, 黄怿. 基于EFPI的数字配电网超声检测传感器[J]. 中国电力, 2023, 56(6): 194-201.
[2]	冯洋, 周磊, 马全福, 尹松, 王朔. 检测GIS局部放电的小型化多频带复合天线[J]. 中国电力, 2023, 56(4): 167-174.
[3]	黄辉, 杨智豪, 魏建国, 邓辉, 刘诣. 变压器套管局部放电射频信号在外部空间的分布特征[J]. 中国电力, 2023, 56(4): 175-183.
[4]	史强, 刘鹍, 李金嵩, 李福超. 基于t-SNE与CFSFDP算法的多源局部放电脉冲分类技术[J]. 中国电力, 2022, 55(5): 102-110.
[5]	谢荣斌, 杨超, 申强, 李诗勇, 罗勇, 于泊宁. TEV与HFCT法测量开关柜局部放电的特性对比[J]. 中国电力, 2022, 55(3): 37-47.
[6]	张利, 屈斌, 王永宁, 陈荣, 刘宝稳, 张少杰, 韩斌, 马宏忠. GIS壳体振动机理和局部放电优化诊断与应用[J]. 中国电力, 2022, 55(3): 80-86.
[7]	郁琦琛, 罗林根, 吴凡, 盛戈皞, 江秀臣. 基于广义回归神经网络的特高频局部放电定位法[J]. 中国电力, 2021, 54(2): 11-17.
[8]	黄雪莜, 熊俊, 张宇, 刘辉, 陈鹭, 孟祥麟, 江秀臣. 基于残差卷积神经网络的开关柜局部放电模式识别[J]. 中国电力, 2021, 54(2): 44-51.
[9]	闫帅, 李朋宇, 王高洁, 李强, 吴凡, 罗林根. 基于特高频无线智能传感阵列的敞开式变电站放电定位方法[J]. 中国电力, 2021, 54(2): 52-57,65.
[10]	李巍巍, 朱轲, 邓元实, 王子康, 黄永禄, 周凯. 温度对电缆附件界面缺陷处局放引发影响机制研究[J]. 中国电力, 2021, 54(11): 181-189.
[11]	张成, 王卫东, 杨延滨, 赵洋, 周弋, 张竟成. 新型电缆多参量带电检测装置设计与应用[J]. 中国电力, 2021, 54(10): 144-151.
[12]	王利, 张伟, 罗定南. 基于随机奇异值分解的局部放电脉冲提取及去噪技术[J]. 中国电力, 2021, 54(10): 196-203.
[13]	田宇, 罗沙, 李宾宾, 孙文. 采用Fisher线性判别法提取GIS内部局部放电信号最优能量特征[J]. 中国电力, 2019, 52(9): 93-101.
[14]	栾贻青, 李建祥, 李超英, 黄锐, 吕俊涛. 高压开关柜局部放电检测机器人的开发与应用[J]. 中国电力, 2019, 52(3): 169-176.
[15]	范敏, 叶会生, 段肖力, 黄海波. 真型开关柜模拟缺陷下的局部放电试验研究[J]. 中国电力, 2019, 52(2): 134-138,143.

模态框（Modal）标题

灰度纹理与油气特征融合的油纸绝缘老化状态评估

Aging State Evaluation of Oil-Paper Insulation Based on Fusion of Gray Texture and Oil Gas Features

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics