基于自适应卡尔曼滤波残差分析的雷击故障定位

雷击是引起电力传输和配电系统中瞬态、故障和停电的重要原因。为提高在含噪环境下雷击故障点检测的准确性与可靠性，提出了一种基于最大似然(KF-ML)卡尔曼滤波残差分析的雷击故障定位方法。首先通过比较短路故障电流与雷击故障电流之间的瞬态特性差异来区分出短路故障和雷击故障。当行波波头到达时，估计的滤波残差呈现出尖锐的奇异性。通过比较初始行波到达两端的时间，确定雷击侧和故障侧，进一步根据双端测距法可以计算出雷击点距离。同时通过初始雷击行波到达故障侧的时间和来自故障点的反射波时间来获得故障点距离。通过模拟雷电流的仿真分析表明了该方法能有效检测到行波波头，并将其应用在不同条件下的雷击定位和故障定位测试中，且灵敏度高。

Lightning strike fault location based on adaptive Kalman filter residual analysis

Lightning strikes are a significant cause of transient, faults and outages in electric power transmission and distribution systems. To improve the accuracy and reliability of lightning stroke fault detection in a noisy environment, a locating method based on Kalman Filter Maximum Likelihood (KF-ML) is proposed. First, short-circuit faults and lightning faults are distinguished by comparing the difference of transient characteristics between their currents. The filter residual will show a sharp singularity when the traveling wave arrives. In addition, the lightning strike side and fault side can be determined by comparing the time when the initial traveling wave reaches both ends. In accordance with the two-terminal distance measurement method, the distance of the lightning strike point can be calculated. The fault distance can be obtained by calculating the time that the initial lightning wave reaches the fault side and the time of the reflected wave from the fault point. The simulation analysis of the simulated lightning current shows that this method can effectively detect the traveling wave head and apply it to the lightning strike location and fault location test under different conditions, and the sensitivity is high. This work is supported by National Natural Science Foundation of China (No. 51507015 and No. 61673388) and Natural Science Foundation of Hunan Province (No. 2018JJ2439).