  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.
