基于特定频率电流波形特征的高压直流线路故障判别方法

发布时间：2019-01-09 12:00

【摘要】：为提高高压直流线路故障判别的可靠性,提出了一种高压直流线路单端故障判别方法。通过分析直流滤波环节的阻抗特性,选取特征谐波电流,并分析其波形特征。直流线路区内、外故障时,特征谐波电流的波动特性差异明显,据此形成直流线路故障判据。利用方差值与均值之比,即波动系数来描述电流波形的波动大小。直流线路的故障判别仅需通过计算特征谐波电流的波动系数来实现。该故障判别方法原理简单、可靠,不受系统参数及干扰的影响。对比分析显示,该故障判别方法能有效克服传统故障判别方法中仅利用单次暂态谐波幅值和频带暂态电气量进行故障识别的可靠性低和易受干扰的缺陷,并在采用较少判据的情况下,仍具有较高的可靠性。大量仿真结果表明,该故障判别方法能可靠、快速地区分直流线路区内、外故障,且能实现故障选极。
[Abstract]:In order to improve the reliability of fault identification of HVDC transmission line, a single terminal fault discrimination method for HVDC line is proposed. By analyzing the impedance characteristics of DC filter, the characteristic harmonic current is selected and its waveform characteristics are analyzed. The fluctuation characteristics of the characteristic harmonic current are obviously different when the DC line is out of fault and the fault criterion of the DC line is formed based on the difference between the fluctuation characteristics of the characteristic harmonic current and the external fault. The ratio of variance to mean, that is, fluctuation coefficient, is used to describe the fluctuation of current waveform. The fault identification of DC line can only be realized by calculating the fluctuation coefficient of characteristic harmonic current. The method is simple, reliable and independent of system parameters and interference. The comparative analysis shows that this method can effectively overcome the shortcomings of the traditional fault discrimination method, which only uses the single transient harmonic amplitude and the frequency band transient electrical quantity to identify the fault, which is low reliability and vulnerable to interference. In the case of using fewer criteria, the reliability is still high. A large number of simulation results show that the fault discrimination method is reliable and can quickly distinguish the fault in the DC line area and the external fault, and the fault pole selection can be realized.
【作者单位】：电力传输与功率变换控制教育部重点实验室(上海交通大学);
【基金】：国家自然科学基金(51377104,51407115) 中国博士后科学基金(2014M560333)资助项目
【分类号】：TM773

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