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文章摘要
新型信号处理方法VMD在电能计量故障识别中的应用
Application of new signal processing method VMD in fault recognition of power metering
Received:May 23, 2019  Revised:May 23, 2019
DOI:DOI: 10.19753/j.issn1001-1390.2020.10.011
中文关键词: 电能计量  信号处理  变分模态分解  故障识别
英文关键词: power  metering, signal  processing, VMD, fault  recognition
基金项目:国家电网有限公司科技资助项目
Author NameAffiliationE-mail
Lu Shufeng Key Laboratory of Electric Energy Measurement,State Grid Co.,Ltd(Electric Power Research Institute,State Grid Jiangsu Electric Power Co., Ltd) 954063989@qq.com 
Li Zhixin* Key Laboratory of Electric Energy Measurement,State Grid Co.,Ltd(Electric Power Research Institute,State Grid Jiangsu Electric Power Co., Ltd) zxli_epc@163.com 
Huang Qifeng Key Laboratory of Electric Energy Measurement,State Grid Co.,Ltd(Electric Power Research Institute,State Grid Jiangsu Electric Power Co., Ltd) hqfyqhqy@126.com 
Yang Shihai Key Laboratory of Electric Energy Measurement,State Grid Co.,Ltd(Electric Power Research Institute,State Grid Jiangsu Electric Power Co., Ltd) ysh.young@163.com 
Fan Jie Key Laboratory of Electric Energy Measurement,State Grid Co.,Ltd(Electric Power Research Institute,State Grid Jiangsu Electric Power Co., Ltd),Electric Power Research Institute,State Grid Jiangsu Electric Power Co., Ltd fanjie121@tom.com 
Wu Qiao Key Laboratory of Electric Energy Measurement,State Grid Co.,Ltd(Electric Power Research Institute,State Grid Jiangsu Electric Power Co., Ltd) wuqiao_2016@163.com 
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中文摘要:
      高压电力计量中出现的多种故障严重影响到计量系统的准确性和可靠性。因此,应当及时准确地判断识别故障种类,排除故障源,恢复计量系统的正常运行。文中采用变分模态分解方法,解析故障时产生的多种频率噪声混叠而成的信号。经仿真与实际数据验证表明,该方法能够有效提取复杂信号中的不同模态,进而区分故障信号与正常信号,在故障识别应用中相比其他方法具有优越性。
英文摘要:
      Many kinds of faults in high voltage power metering seriously affect the accuracy and reliability of the metering system. It is necessary to judge and identify the types of faults timely and accurately, eliminate the source of faults and restore the normal operation of the metering system. In this paper, the variational mode decomposition method is used to analyse the signals generated by the mixing of various frequency noises. The simulation and actual data validation show that the method can effectively extract different modes of complex signals, and then distinguish fault signals from normal signals, so as to improve the accuracy of fault identification.
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