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Identification Method for Voltage Sags Based on K-means-Singular Value Decomposition and Least Squares Support Vector Machine

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  • Haoyuan Sha

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China
    Jiangsu Key Laboratory of Smart Grid Technology and Equipment, Southeast University, Nanjing 210096, China)

  • Fei Mei

    (Jiangsu Key Laboratory of Smart Grid Technology and Equipment, Southeast University, Nanjing 210096, China
    College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Chenyu Zhang

    (State Grid Jiangsu Electric Power Co., Ltd. Research Institute, Nanjing 211113, China)

  • Yi Pan

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China
    Jiangsu Key Laboratory of Smart Grid Technology and Equipment, Southeast University, Nanjing 210096, China)

  • Jianyong Zheng

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China
    Jiangsu Key Laboratory of Smart Grid Technology and Equipment, Southeast University, Nanjing 210096, China)

Abstract

Voltage sag is one of the most serious problems in power quality. The occurrence of voltage sag will lead to a huge loss in the social economy and have a serious effect on people’s daily life. The identification of sag types is the basis for solving the problem and ensuring the safe grid operation. Therefore, with the measured data uploaded by the sag monitoring system, this paper proposes a sag type identification algorithm based on K-means-Singular Value Decomposition (K-SVD) and Least Squares Support Vector Machine (LS-SVM). Firstly; each phase of the sag sample RMS data is sparsely coded by the K-SVD algorithm and the sparse coding information of each phase data is used as the feature matrix of the sag sample. Then the LS-SVM classifier is used to identify the sag type. This method not only works without any dependence on the sag data feature extraction by artificial ways, but can also judge the short-circuit fault phase, providing more effective information for the repair of grid faults. Finally, based on a comparison with existing methods, the accuracy advantages of the proposed algorithm with be presented.

Suggested Citation

  • Haoyuan Sha & Fei Mei & Chenyu Zhang & Yi Pan & Jianyong Zheng, 2019. "Identification Method for Voltage Sags Based on K-means-Singular Value Decomposition and Least Squares Support Vector Machine," Energies, MDPI, vol. 12(6), pages 1-15, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:6:p:1137-:d:216644
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    Citations

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    Cited by:

    1. K. Padmanathan & N. Kamalakannan & P. Sanjeevikumar & F. Blaabjerg & J. B. Holm-Nielsen & G. Uma & R. Arul & R. Rajesh & A. Srinivasan & J. Baskaran, 2019. "Conceptual Framework of Antecedents to Trends on Permanent Magnet Synchronous Generators for Wind Energy Conversion Systems," Energies, MDPI, vol. 12(13), pages 1-39, July.
    2. Xing Zhang & Chongchong Zhang & Zhuoqun Wei, 2019. "Carbon Price Forecasting Based on Multi-Resolution Singular Value Decomposition and Extreme Learning Machine Optimized by the Moth–Flame Optimization Algorithm Considering Energy and Economic Factors," Energies, MDPI, vol. 12(22), pages 1-23, November.
    3. Radovan Turović & Dinu Dragan & Gorana Gojić & Veljko B. Petrović & Dušan B. Gajić & Aleksandar M. Stanisavljević & Vladimir A. Katić, 2022. "An End-to-End Deep Learning Method for Voltage Sag Classification," Energies, MDPI, vol. 15(8), pages 1-22, April.
    4. Mario Šipoš & Zvonimir Klaić & Emmanuel Karlo Nyarko & Krešimir Fekete, 2021. "Determining the Optimal Location and Number of Voltage Dip Monitoring Devices Using the Binary Bat Algorithm," Energies, MDPI, vol. 14(1), pages 1-13, January.

    More about this item

    Keywords

    voltage sag; RMS; K-SVD; LS-SVM;
    All these keywords.

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