Fisher Matrix Based Fault Detection for PMUs Data in Power Grids

TL;DR

This paper introduces Fisher matrix-based methods for fault detection in power grid PMU data, offering faster and more accurate detection by analyzing covariance matrices and spectral statistics.

Contribution

The paper proposes novel Fisher matrix-based fault detection methods that improve speed and accuracy over existing techniques in power grid monitoring.

Findings

The methods reduce computational time compared to existing approaches.

They achieve higher fault detection accuracy.

The spectral analysis techniques enable faster fault identification.

Abstract

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.