Islanding Detection based on Nonparametric Data Reduction Techniques and Statistical Quality Control

In this paper, a wide-area, measurement-based islanding detection technique based on nonlinear, nonparametric data reduction methods and statistical quality control charts is presented. The method exploits the spectral properties of a probability transition matrix of similarities measures on updated observation window, and accounts for nonlinearities in the measured signals. When combined with a spectral change detection algorithm, changes in the measured data leading to island formation and deviations from data driven (upper and lower) control limits are computed in an automated manner. Detailed time-domain simulations are conducted to test the ability of the method to detect islanding conditions.