论文标题
Fisher矩阵基于电网中PMU数据的故障检测
Fisher Matrix Based Fault Detection for PMUs Data in Power Grids
论文作者
论文摘要
异常事件检测对于电力系统的安全操作至关重要。在本文中，提出了基于Fisher随机矩阵的两种方法来检测功率网格中的故障。首先，构建了故障检测矩阵，并将事件检测问题重新格式化为两样本协方差矩阵测试问题。其次，得出了Fisher基质的线性光谱统计量的中心极限定理，并提出了用于测试故障的测试统计量。为了节省计算资源，基于测试统计量的故障间隔的筛选步骤旨在检查故障的存在。然后提出了两种逐点的方法，以确定间隔中故障的时间。一种方法通过检查最大的样品特征值是否属于标准Fisher矩阵的限制光谱分布之外，可以检测到故障，该矩阵的限制光谱分布可以以更高的精度检测到故障。另一种方法根据提出的统计量测试故障，该故障的检测速度更快。与现有作品相比，模拟结果表明，本文提出的两种方法的计算时间较小，并且提供了更高的准确性。
Abnormal event detection is critical in the safe operation of power system. In this paper, using the data collected from phasor measurement units (PMUs), two methods based on Fisher random matrix are proposed to detect faults in power grids. Firstly, the fault detection matrix is constructed and the event detection problem is reformatted as a two-sample covariance matrices test problem. Secondly, the central limit theorem for the linear spectral statistic of the Fisher matrix is derived and a test statistic for testing faults is proposed. To save computing resources, the screening step of fault interval based on the test statistic is designed to check the existence of faults. Then two point-by-point methods are proposed to determine the time of the fault in the interval. One method detects faults by checking whether the largest sample eigenvalue falls outside the supporting set of limiting spectral distribution of the standard Fisher matrix, which can detect the faults with higher accuracy. The other method tests the faults based on the statistic proposed, which has a faster detection speed. Compared with existing works, the simulation results illustrate that two methods proposed in this paper cost less computational time and provide a higher degree of accuracy.
