张超, 王宾, 张海, 刘宗杰, 朱海鹏, 袁冰. 中性点直接接地电网单相串联铁磁谐振检测与类型辨识[J]. 电力系统自动化, 2022, 46(19): 172-179.
引用本文: 张超, 王宾, 张海, 刘宗杰, 朱海鹏, 袁冰. 中性点直接接地电网单相串联铁磁谐振检测与类型辨识[J]. 电力系统自动化, 2022, 46(19): 172-179.
ZHANG Chao, WANG Bin, ZHANG Hai, LIU Zongjie, ZHU HaiPeng, YUAN Bing. Detection and Type Identification of Single-phase Series Ferroresonance in Neutral Point Solid Grounding Power Grid[J]. Automation of Electric Power Systems, 2022, 46(19): 172-179.
Citation: ZHANG Chao, WANG Bin, ZHANG Hai, LIU Zongjie, ZHU HaiPeng, YUAN Bing. Detection and Type Identification of Single-phase Series Ferroresonance in Neutral Point Solid Grounding Power Grid[J]. Automation of Electric Power Systems, 2022, 46(19): 172-179.

中性点直接接地电网单相串联铁磁谐振检测与类型辨识

Detection and Type Identification of Single-phase Series Ferroresonance in Neutral Point Solid Grounding Power Grid

  • 摘要: 铁磁谐振表现出来的非线性波形特征是实施有效检测的重要依据,但是由于缺少谐振回路的解析解,难以给出合理的检测阈值;再加上实际系统中还存在与谐振波形特征类似的故障或扰动,比如弧光高阻接地故障,现有的铁磁谐振检测方法准确性不高。对比分析了中性点直接接地系统单相串联铁磁谐振和弧光高阻接地故障模型,通过分段线性化获取了状态方程时域解析解。设计了一种基于相电压频谱和相电流均值包络线特征共同辨识的检测方法,并利用伏安特性进一步辨识弧光高阻接地故障与工频铁磁谐振。最后,仿真和现场录波数据验证了检测算法的有效性。

     

    Abstract: The nonlinear waveform characteristic of ferroresonance is an important basis for effective detection. However, due to the lack of analytical solution of resonance circuit, it is difficult to give a reasonable detection threshold. In addition, there are faults or disturbances similar to the characteristics of resonance waveform in the actual system, such as arc high-impedance grounding fault, and the accuracy of the existing ferroresonance detection methods is not high. In this paper, the single-phase series ferroresonance and arc high-impedance grounding fault models of neutral point solid grounding system are compared and analyzed,and the time-domain analytical solution of the state equation is obtained by piecewise linearization. A detection method based on the common identification of phase voltage spectrum and phase current mean envelope is designed, and the arc high-impedance grounding fault and power frequency ferromagnetic resonance are further identified by using volt-ampere characteristics. Finally,simulation and field recording data verify the effectiveness of the detection algorithm.

     

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