秦雪,刘亚东,孙鹏,王鹏,江秀臣.基于故障波形时频特征配网故障识别方法研究[J].仪器仪表学报,2017,38(1):41-49
基于故障波形时频特征配网故障识别方法研究
Study on the line fault root cause identification method in distribution networks based on time frequency characteristics of fault waveforms
  
DOI:
中文关键词:  配电网  故障原因  时频特性  电弧模型  识别方法
英文关键词:distribution network  fault root cause  time frequency characteristic  arc model  identification method
基金项目:国家自然科学基金(51307109)、国家电网公司科技项目(SGTYHT/14 JS 188)资助
作者单位
秦雪 上海交通大学电子信息与电气工程学院上海200240 
刘亚东 上海交通大学电子信息与电气工程学院上海200240 
孙鹏 云南电力科学研究院昆明650000 
王鹏 国网河南省电力公司电力科学研究院郑州450052 
江秀臣 上海交通大学电子信息与电气工程学院上海200240 
AuthorInstitution
Qin Xue School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China 
Liu Yadong School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China 
Sun Peng Yunnan Electric Power Research Institute, Kunming 650000, China 
Wang Pen Electric Power Research Institute, Henan Electric Power Company, State Grid, Zhengzhou 450052, China 
Jiang Xiuchen School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China 
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中文摘要:
      配电线路故障类型的准确识别可为线路运维人员提供方向性的指导。基于对故障波形时频特征的分析,提出了一种配电线路故障类型识别方法,通过对不同类型故障波形建立模型与理论分析,从时域、频域和电弧3个方面提出可以表征不同故障类型波形特点的特征参量;并提出依据故障波形数据提取特征参量的计算公式,将多参量融合作为依据建立识别逻辑,通过对输入波形数据特征量的检测归类,实现对不同原因引起的配网线路故障类型的自动识别。最终利用美国电力研究协会(EPRI)提供的136组现场故障波形数据对算法进行了闭环控制与验证,结果显示识别成功率达到90%,证实了利用故障波形时频特性实现配电线路故障类型识别的可行性。
英文摘要:
      Accurate identification of distribution network fault type can provide directive guidance for the operation and maintenance personals of transmission lines. In this paper, a new fault root cause identification method of distribution network based on the time and frequency characteristic analysis of fault waveforms is proposed. Through model building and theoretical analysis of different types of fault waveforms, the characteristic parameters are proposed, which can characterize different kinds of fault waveforms from time domain, frequency domain and arc model. The formulas for calculating characteristic parameters from fault waveform data are proposed. Multiple characteristic parameters are fused, and based on which the classifier is built; the fault types of the distribution network caused by different root causes are identified automatically through detecting and analyzing the characteristic parameters of the input waveform data. Finally, the proposed classification method was tested and verified using 136 groups of different field fault waveform data provided by EPRI; the test results show that the successful identification ratio reaches to 90%, which verifies the feasibility of using fault waveform time and frequency characteristics to realize the fault type identification of distribution networks.
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