灰色关联分析在变压器DGA缺码数据中的应用研究
Application Research of Grey Relational Analysis in Transformer DGA Code Absence Data
DOI: 10.12677/JEE.2018.64037, PDF,   
作者: 尚西华, 张治平, 王会琳, 周 鑫:国网河南省电力公司检修公司,河南 郑州
关键词: 变压器DGA三比值法故障诊断Power Transformer DGA Three-Ratio Method Fault Diagnosis
摘要: 三比值法是电力变压器进行潜伏性故障诊断的有效方法之一,但该方法存在缺码问题。本文根据收集到的DGA数据,发现相同故障样本中特征气体的增减变化趋势相同,不同故障特征气体变化趋势有明显不同。本文通过挖掘DGA中各数据的变化趋势,运用数据灰色关联度理论,使用基于灰色关联度的DGA故障识别方法,对DGA缺码故障变压器进行故障诊断。弥补了三比值法的不足,提高变压器故障诊断精度,研究成果具有重要的理论价值和工程实用价值,可以大力推广,给电力系统及社会带来较大的经济效益。
Abstract: Three-ratio method is one of the effective methods for latent fault diagnosis of power transfor-mers, but there is a lack of code in this method. According to the DGA data collected in this paper, it is found that the variation trend of the characteristic gas in the same fault samples is the same, and the variation trend of different fault features is obviously different. In this paper, by excavate the changing trend of each data in DGA, using the grey relational theory and using the DGA fault identification method based on grey relational analysis, the fault diagnosis of code absence power transformers in DGA is carried out. It makes up for the deficiency of the three ratio method and improves the accuracy of transformer fault diagnosis. The research results have important theoretical value and practical value of engineering. It can be popularized vigorously and bring great economic benefits to the power system and the society.
文章引用:尚西华, 张治平, 王会琳, 周鑫. 灰色关联分析在变压器DGA缺码数据中的应用研究[J]. 电气工程, 2018, 6(4): 325-331. https://doi.org/10.12677/JEE.2018.64037

参考文献

[1] 杜正聪, 牛高远. 基于加权模糊聚类算法的变压器故障诊断方法[J]. 高压电器, 2014, 50(4): 42-48.
[2] 杨延方, 张航, 黄立滨, 等. 基于改进主成分分析的电力变压器潜伏性故障诊断[J]. 电力自动化设备, 2015, 35(6): 149-165.
[3] GB/T7252-2001 变压器油中溶解气体分析和判断导则[S].
[4] 宋斌, 刘志雄, 李恩文, 等. 基于负关联度的DGA故障诊断分析[J]. 电网技术, 2015, 39(9): 2627-2632.
[5] 葛乐, 陆文伟, 周志成, 等. 基于改进熵权法和灰色关联分析的变压器故障诊断[J]. 电测与仪表, 2016, 53(12): 46-51.
[6] 蔡金锭, 黄云程. 基于灰色关联诊断模型的电力变压器绝缘老化研究[J]. 高电压技术, 2015, 41(10): 3296-3301.
[7] Singh, J. and Sood, Y.R. (2007) Dissolved Gas Analysis for Power Transformers. Electr. India.
[8] 宋臻杰. 基于灰色关联分析的变压器油纸绝缘状态评估研究[D]: [硕士学位论文]. 成都: 西南交通大学, 2017.
[9] Bigdeli, M., Vakilian, M. and Rahimpour, E. (2012) Transformer Winding Faults Classification Based on Transfer Function Analysis by Support Vector Machine. IET Electric Power Applications, 6/5, 268-276. [Google Scholar] [CrossRef
[10] 张安平. 基于熵权优化灰色关联度方法的电力变压器故障诊断研究[D]: [硕士学位论文]. 南京: 南京理工大学. 2016.
[11] Dong, L., Xiao, D., Liang, Y., et al. (2008) Rough Set and Fuzzy Wavelet Neural Network Integrated with Least Square Weighted Fusion Algorithm Based Fault Diagnosis Research for Power Transformers. Electric Power Systems Research, 78, 129-136. [Google Scholar] [CrossRef
[12] 刘思峰. 灰色系统理论及其应用[M]. 北京: 科学出版社, 2008: 22-65.
[13] 孙鹏霄. 灰色关联方法的分析与应用[J]. 数学的实践与认识, 2014, 44(1): 97-101.
[14] 张卫华, 苑津莎, 张铁峰, 等. 应用B样条理论改进的变压器三比值故障诊断方法. 中国电机工程学报, 2014, 34(24): 4129-4135.
[15] Zhao, J.Y., Zheng, R.R., Li, J.P. (2009) Transformer Fault Diagnosis Based on Homotopy BP Algorithm. 9th International Conference on Electronic Measurement & Instruments (ICEMI), 4, 622-626. [Google Scholar] [CrossRef
[16] 李硕, 赵峰. 基于熵权优化加权灰色关联度的变压器故障诊断方法[J]. 变压器, 2013, 37(1): 136-142.
[17] Barbosa, F.R. and Braga, A.P.S. (2012) Application of an Artificial Neural Network in the Use of Physicochemical Properties as a Low Cost Proxy of Power Transformers DGA Data. IEEE Transactions on Dielectrics and Electrical Insulation, 19, 239-246. [Google Scholar] [CrossRef
[18] 张东波, 徐瑜 王耀南. 主动差异学习神经网络集成方法在变压器DGA故障诊断中的应用[J]. 中国电机工程学报, 2010, 30(22): 64-70.
[19] 邹杰慧. 基于新型编码隶属函数的变压器故障模糊诊断法[J]. 电力系统自动化设备, 2010, 30(7): 88-91.