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Power Quality Prediction, Early Warning, and Control for Points of Common Coupling with Wind Farms

Author

Listed:
  • Jingjing Bai

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Wei Gu

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Xiaodong Yuan

    (Jiangsu Electrical Power Company Research Institute, Nanjing 210096, China)

  • Qun Li

    (Jiangsu Electrical Power Company Research Institute, Nanjing 210096, China)

  • Feng Xue

    (Dongguan Power Supply Bureau, Dongguan 523000, China)

  • Xuchong Wang

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

Abstract

Wind farms can affect the power quality (PQ) of the power supply grid, with subsequent impacts on the safe and stable operation of other electrical equipment. A novel PQ prediction, early warning, and control approach for the common coupling points between wind farms and the network is proposed in this paper. We then quantify PQ problems and provide rational support measures. To obtain predicted PQ data, we first establish a trend analysis model. The model incorporates a distance-based cluster analysis, probability distribution analysis based on polynomial fitting, pattern matching based on similarity, and Monte Carlo random sampling. A data mining algorithm then uses the PQ early warning flow to analyze limit-exceeding and abnormal data, quantify their severity, and output early warning prompts. Finally, PQ decision support is applied to inform both the power suppliers and users of anomalous changes in PQ, and advise on corresponding countermeasures to reduce relevant losses. Case studies show that the proposed approach is effective and feasible, and it has now been applied to an actual PQ monitoring platform.

Suggested Citation

  • Jingjing Bai & Wei Gu & Xiaodong Yuan & Qun Li & Feng Xue & Xuchong Wang, 2015. "Power Quality Prediction, Early Warning, and Control for Points of Common Coupling with Wind Farms," Energies, MDPI, vol. 8(9), pages 1-18, August.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:9:p:9365-9382:d:55048
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    References listed on IDEAS

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    1. Nantian Huang & Shuxin Zhang & Guowei Cai & Dianguo Xu, 2015. "Power Quality Disturbances Recognition Based on a Multiresolution Generalized S-Transform and a PSO-Improved Decision Tree," Energies, MDPI, vol. 8(1), pages 1-24, January.
    2. James B. Mcdonald & Jeff Sorensen & Patrick A. Turley, 2013. "Skewness And Kurtosis Properties Of Income Distribution Models," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 59(2), pages 360-374, June.
    3. Meng-Hui Wang & Her-Terng Yau, 2014. "New Power Quality Analysis Method Based on Chaos Synchronization and Extension Neural Network," Energies, MDPI, vol. 7(10), pages 1-18, October.
    4. Nicolae Golovanov & George Cristian Lazaroiu & Mariacristina Roscia & Dario Zaninelli, 2013. "Power Quality Assessment in Small Scale Renewable Energy Sources Supplying Distribution Systems," Energies, MDPI, vol. 6(2), pages 1-12, January.
    5. Pedro Roncero-Sànchez & Enrique Acha, 2014. "Design of a Control Scheme for Distribution Static Synchronous Compensators with Power-Quality Improvement Capability," Energies, MDPI, vol. 7(4), pages 1-22, April.
    6. Mingchao Xia & Xiaoliang Li, 2013. "Design and Implementation of a High Quality Power Supply Scheme for Distributed Generation in a Micro-Grid," Energies, MDPI, vol. 6(9), pages 1-21, September.
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    Cited by:

    1. Yanjian Peng & Yong Li & Zhisheng Xu & Ming Wen & Longfu Luo & Yijia Cao & Zbigniew Leonowicz, 2016. "Power Quality Improvement and LVRT Capability Enhancement of Wind Farms by Means of an Inductive Filtering Method," Energies, MDPI, vol. 9(4), pages 1-18, April.
    2. Misael Lopez-Ramirez & Luis Ledesma-Carrillo & Eduardo Cabal-Yepez & Carlos Rodriguez-Donate & Homero Miranda-Vidales & Arturo Garcia-Perez, 2016. "EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments," Energies, MDPI, vol. 9(7), pages 1-15, July.

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