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文章摘要
融合遥信报警和电气量分析的电网故障诊断方法
Fault Diagnosis Approach for Power Grid with Fusion of Telecommunication Alarm and Electrical Measurements Analysis
Received:August 10, 2013  Revised:December 23, 2013
DOI:
中文关键词: 故障诊断  信息融合  模糊解析模型  希尔伯特黄变换  模糊K-均值
英文关键词: fault diagnosis  information fusion  fuzzy analytic model  Hilbert-Huang transform  fuzzy K-means
基金项目:云南电网智能在线故障诊断和恢复处理应用研究与实施
Author NameAffiliationE-mail
WEN Qing-feng* School of Electrical and Electronic Engineering,North China Electric Power University wqf_ncepu@126.com 
LI Wen-yun Yunnan Power Grid Corporation Dispatching Control Center  
GU Xue-ping School of Electrical and Electronic Engineering,North China Electric Power University  
ZHU Tao Yunnan Power Grid Corporation Dispatching Control Center  
ZHAO Chuan Yunnan Power Grid Corporation Dispatching Control Center  
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中文摘要:
      随着广域测量系统和故障录波信息网的广泛应用,系统的电气量信息获取也越来越方便可靠。本文利用融合开关量和电气量信息的综合故障诊断方法,首先分析报警信息中的开关量信息,搜索停电区域得到可疑故障设备集,对可疑设备进行模糊解析模型求解分析以及相关电气量的希尔伯特黄变换分析。定义了元件的解析故障度、频率畸变度和能量变化度三个故障测度,将这三者作为证据体采用改进D-S证据理论进行融合得到最终诊断结果。算例表明,该方法能有效降低保护和断路器拒动、误动的影响,提高故障诊断结果的准确度。
英文摘要:
      With more application of wide area measurement systems and fault recording networks to power systems, the electrical measurements can be obtained more convenient and reliable. This paper uses a comprehensive fault diagnosis method based on switching-status alarms and electrical measurements. By analyzing the switching-status data in alarm information and finding out the outage area, the suspicious fault element set can be determined. The relevant data is analyzed through fuzzy analytic model and Hilbert-Huang transform. The analytic fault degree、frequency distortion degree and energy variation degree are defined as indices of fault diagnosis. The three indices are fused as evidence forms by the improved D-S evidence theory to obtain the diagnosis results. Simulation and calculations show that the proposed method is effective of reducing the influence resulted from relay or breaker’s mal-operation or fail-operation. The presented approach may significantly improve the diagnostic accuracy of power grid faults.
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