中文摘要：

针对目前超高压线路中所用选相方法不能快速准确地识别所有故障类型的问题，提出一种基于电压故障分量和卡尔曼滤波算法的新型选相方法。该方法定义每相电压故障分量和其余两相电压故障分量差值的比值为故障相识别系数。通过研究该系数在不同故障条件下的变化特征，可实现快速选相的目的。仿真结果表明，在高压线路故障中利用卡尔曼滤波算法提取基波相量速度快、准确率高。同时该选相算法受过渡电阻、故障位置、故障初相角的影响很小，在半周波内可准确选出故障相，且在强弱电源侧均具有较高的选相灵敏度。

英文摘要：

Since the existing fault phase identification methods can not identify all fault types quickly and accurately for EHV transmission lines, this paper proposes a new method of fault phase identification based on the fault component of phase voltage difference and the Kalman filter algorithm. The method defines the fault component＇s ratio of one phase voltage to the difference of the other two phase voltages as a fault phase identification coefficient. By analyzing the characteristics of fault phase identification factors in each fault type, the fault phase can be identified. Simulation results show that using the Kalman filter algorithm to extract fundamental component is faster and more accurate. Meanwhile, the method can identify fault phases within half a cycle and is scarcely influenced by fault resistances, fault locations and fault initial phase angles. It also has a high sensitivity when the fault is on the side of strong source.