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)

Huaishuo Xiao, Jianchun Wei and Qingquan Li
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Huaishuo Xiao: Department of Electrical Engineering, Shandong University, Jinan 250061, China
Jianchun Wei: Department of Electrical Engineering, Shandong University, Jinan 250061, China
Qingquan Li: Department of Electrical Engineering, Shandong University, Jinan 250061, China

Energies, 2017, vol. 10, issue 11, 1-16

Abstract: 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.

Keywords: power quality; combined disturbance; singular value decomposition (SVD); least squares-estimation of signal parameters via rotational invariance techniques (TLS-ESPRIT); parameter identification (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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