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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)

Author

Listed:
  • Huaishuo Xiao

    (Department of Electrical Engineering, Shandong University, Jinan 250061, China
    Shandong Provincial Key Laboratory of UHV Transmission Technology and Equipment, 17923 Jingshi Road, Jinan 250061, China)

  • Jianchun Wei

    (Department of Electrical Engineering, Shandong University, Jinan 250061, China
    Shandong Provincial Key Laboratory of UHV Transmission Technology and Equipment, 17923 Jingshi Road, Jinan 250061, China)

  • Qingquan Li

    (Department of Electrical Engineering, Shandong University, Jinan 250061, China
    Shandong Provincial Key Laboratory of UHV Transmission Technology and Equipment, 17923 Jingshi Road, Jinan 250061, China)

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.

Suggested Citation

  • Huaishuo Xiao & Jianchun Wei & Qingquan Li, 2017. "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)," Energies, MDPI, vol. 10(11), pages 1-16, November.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:11:p:1809-:d:118173
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    Cited by:

    1. Kewei Cai & Belema Prince Alalibo & Wenping Cao & Zheng Liu & Zhiqiang Wang & Guofeng Li, 2018. "Hybrid Approach for Detecting and Classifying Power Quality Disturbances Based on the Variational Mode Decomposition and Deep Stochastic Configuration Network," Energies, MDPI, vol. 11(11), pages 1-18, November.
    2. Raoult Teukam Dabou & Innocent Kamwa & Jacques Tagoudjeu & Francis Chuma Mugombozi, 2021. "Sparse Signal Reconstruction on Fixed and Adaptive Supervised Dictionary Learning for Transient Stability Assessment," Energies, MDPI, vol. 14(23), pages 1-20, November.

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