Identification of Combined Power Quality Disturbances Using Singular Value Decomposition (SVD) and Total Least Squares-Estimation of Signal Parameters via Rotational Invariance Techniques (TLS-ESPRIT)

In order to identify various kinds of combined power quality disturbances, the singular value decomposition (SVD) and the improved total least squares-estimation of signal parameters via rotational invariance techniques (TLS-ESPRIT) are combined as the basis of disturbance identification in this paper. SVD is applied to identify the catastrophe points of disturbance intervals, based on which the disturbance intervals are segmented. Then the improved TLS-ESPRIT optimized by singular value norm method is used to analyze each data segment, and extract the amplitude, frequency, attenuation coefficient and initial phase of various kinds of disturbances. Multi-group combined disturbance test signals are constructed by MATLAB and the proposed method is also tested by the measured data of IEEE Power and Energy Society (PES) Database. The test results show that the new method proposed has a relatively higher accuracy than conventional TLS-ESPRIT, which could be used in the identification of measured data.