基于局部离群因子的PMU连续坏数据检测方法

同步相量测量单元(PMU)能为电力系统监测和控制提供实时数据。然而,PMU连续坏数据与扰动数据高度相似,可能会导致控制中心做出错误的决策。针对PMU连续坏数据难以与扰动数据区分的问题,提出了一种基于局部离群因子(LOF)的连续坏数据检测算法。通过大量现场数据分析得出连续坏数据空间相似性差、扰动数据空间相似性强的结论,依据此结论提出了基于动态时间规整(DTW)的空间相似性评估方法。通过评估不同PMU的空间相似性来计算每台PMU的LOF值,进一步,提出了基于箱线图的阈值确定方法。通过比较当前窗口每台PMU的LOF值是否超过阈值,在线识别连续坏数据。仿真和测试结果表明,所提方法能有效实现连续坏数据的辨识和检测,并区分扰动数据。

Continuous Bad Data Detection Method for PMU Based on Local Outlier Factor

Synchrophasor measurement units (PMUs) can provide real-time measurement data for power system monitoring and control. However, continuous bad data of PMU can be highly similar to the disturbance data, which may cause the control center to make wrong decisions. Aiming at the problem that continuous bad data of PMU is difficult to be distinguished from the disturbance data, a continuous bad data detection algorithm is proposed based on the local outlier factor (LOF). Through analysis of a large amount of measured data, it is concluded that the continuous bad data has poor spatial similarity, and the disturbance data has strong spatial similarity. According to this conclusion, a method for spatial similarity evaluation is proposed based on dynamic time warping (DTW). The LOF value of each PMU is calculated by evaluating the spatial similarity between different PMUs. Further, a method for determining the threshold value based on the box-plot is proposed. By comparing whether the LOF value of each PMU in the current window exceeds the threshold, the continuous bad data can be detected online. Simulation and testing results illustrate that the proposed method can achieve the efficient continuous bad data identification and detection, and the disturbance data can be distinguished.