Open AccessFault Localization for Synchrophasor Data using Kernel Principal Component Analysis
Author(s) -
R. Chen,
Xiang Sun,
G. Liu
Publication year - 2017
Publication title -
advances in electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 23
eISSN - 1844-7600
pISSN - 1582-7445
DOI - 10.4316/aece.2017.04005
Subject(s) - principal component analysis , kernel principal component analysis , component (thermodynamics) , kernel (algebra) , computer science , fault (geology) , data mining , artificial intelligence , pattern recognition (psychology) , kernel method , support vector machine , mathematics , geology , physics , combinatorics , seismology , thermodynamics
In this paper, based on Kernel Principal Component Analysis (KPCA) of Phasor Measurement Units (PMU) data, a nonlinear method is proposed for fault location in complex power systems. Resorting to the scaling factor, the derivative for a polynomial kernel is obtained. Then, the contribution of each variable to the T2 statistic is derived to determine whether a bus is the fault component. Compared to the previous Principal Component Analysis (PCA) based methods, the novel version can combat the characteristic of strong nonlinearity, and provide the precise identification of fault location. Computer simulations are conducted to demonstrate the improved performance in recognizing the fault component and evaluating its propagation across the system based on the proposed method