Low-Complexity Nonlinear Analysis of Synchrophasor Measurements for Events Detection and Localization
This paper is concerned with the computational efficient nonlinear analysis of phasor measurement unit data and presents a Nyström principal components analysis-based algorithm for events detection and localization in an electrical power system. Based on the properly chosen sample subset of every moving window data, the Nyström approximation is carried out to obtain the principal eigenvalues and related eigenvectors of a mapped kernel matrix. Then, the <inline-formula> <tex-math notation="LaTeX">$T^{2}$ </tex-math></inline-formula> statistic is constructed to detect the abnormal states of an electrical power system. In addition, the contribution of each variable to the <inline-formula> <tex-math notation="LaTeX">$T^{2}$ </tex-math></inline-formula> statistic is derived to determine the location of the fault bus. Compared with the previous works, the novel version proposed in this paper efficiently reduces the computational burden, and accurately locates the fault bus. Computer simulations using both realistic data, collected from the China power system, and simulated voltage and phase-angle data, validate the reliability of the proposed algorithm.