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作 者:温才权 韦鑫 王杰 周凯[2] 李轩毅 龚薇[2] WEN Caiquan;WEI Xin;WANG Jie;ZHOU Kai;LI Xuanyi;GONG Wei(Wuzhou Bureau of China Southern Power Grid EHV Transmission Company,Wuzhou 543000,Guangxi,China;School of Electrical Engineering,Sichuan University,Chengdu 610065,Sichuan,China)
机构地区:[1]中国南方电网有限责任公司超高压输电公司梧州局,广西梧州543000 [2]四川大学电气工程学院,四川成都610065
出 处:《电网与清洁能源》2023年第7期80-89,共10页Power System and Clean Energy
基 金:国家自然科学基金项目(52277156)。
摘 要:频域反射法(frequency domain reflection,FDR)是电力电缆故障定位的有效办法,但尚未应用于控制电缆,且现有FDR法存在频域泄露严重、灵敏度低等问题,导致故障定位误差较大。该文提出了采用补充集合经验模态分解(complementary ensemble empirical mode decomposition,CEEMD)与功率谱密度(power spectral density,PSD)联合算法,实现了变电站控制电缆精准定位。首先通过CEEMD对首端反射系数谱实部进行分解,然后对模态分量进行加努塔尔-布莱克曼混合卷积窗(nutall-blackman window)处理;最后对加窗后的模态分量进行PSD分析,获得故障电缆的定位曲线。该文搭建了控制电缆模型进行仿真研究,并选取总长为100 m,在50 m存在故障的ZR-KVVP2-22 4×4型控缆进行实验验证。仿真及实验结果表明:与传统FDR定位方法相比,该文所提方法可以有效抑制频谱泄露,提升故障识别的灵敏度。Frequency Domain Reflection(FDR)is an effective method for fault location of power cables,but it has yet to be applied to control cables.Moreover,existing FDR methods have serious frequency domain leakage and low sensitivity,leading to significant fault location errors.This paper proposes a combined algorithm of Complementary Ensemble Empirical Mode Decomposition(CEEMD)and Power Spectral Density(PSD)to achieve accurate positioning of substation control cables.First,the real part of the head-end reflection coefficient spectrum is decomposed by CEEMD,and then the modal components are processed by Nuttal-Blackman hybrid convolution window;Finally,PSD processing is performed on the windowed modal components to obtain the cable fault location curve.The paper builds a control cable model for simulation research,and selects ZR-KVVP2-224×4 control cable with a total length of 100 m and a fault at 50 m for test verification.The simulation and experimental results show that the proposed method can effectively suppress spectrum leakage and improve the sensitivity of fault identification compared with the traditional FDR location method.
关 键 词:频域反射法 电缆故障定位 频谱泄露 功率谱分析 补充集合经验模态分解
分 类 号:TM726[电气工程—电力系统及自动化]
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