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Optimal Dynamic Reactive Power Reserve for Wind Farms Addressing Short-Term Voltage Issues Caused by Wind Turbines Tripping

Author

Listed:
  • Liang Wu

    (School of Electric Power, South China University of Technology, Guangzhou 510640, China)

  • Lin Guan

    (School of Electric Power, South China University of Technology, Guangzhou 510640, China)

  • Feng Li

    (School of Electric Power, South China University of Technology, Guangzhou 510640, China
    Guangdong Power Grid Co., Ltd., Guangzhou 510080, China)

  • Qi Zhao

    (Logistics Center of Guangdong Power Grid Co., Ltd., Guangzhou 510630, China)

  • Yingjun Zhuo

    (School of Electric Power, South China University of Technology, Guangzhou 510640, China)

  • Peng Chen

    (Electric Power Research Institute of Guangdong Power Grid Co., Ltd., Guangzhou 510080, China)

  • Yaotang Lv

    (Power Dispatching and Control Center of China Southern Grid, Guangzhou 510000, China)

Abstract

In regional power grids with high wind power penetration, wind turbine tripping poses great challenges to short-term voltage stability. Dynamic reactive power (VAR) compensation (DVC) plays an important role in securing wind farm operation. To address short-term voltage stability issues, voltage disturbance index (DI) and voltage supporting index (SI) are defined to evaluate the degree of voltage fluctuation and voltage supporting ability of a bus, respectively. Then corresponding vector-type features, called disturbance vector (DV) and supporting vector (SV) are proposed based on the defined indexes. The Kendall rank correlation coefficient is adopted to evaluate the matching degree of DV and SV, so as to determine the influenced area of each wind farm. Candidate locations for DVC are determined sequentially. By comprehensively considering the probability of combined disturbance in each wind farm, a site selection method is proposed and then genetic algorithm is applied to optimize the DVC capacity considering short-term voltage security. The proposed method is applied on a modified NE 39-bus system and a real power grid. Comparison with the engineering practice-based method validates its effectiveness.

Suggested Citation

  • Liang Wu & Lin Guan & Feng Li & Qi Zhao & Yingjun Zhuo & Peng Chen & Yaotang Lv, 2018. "Optimal Dynamic Reactive Power Reserve for Wind Farms Addressing Short-Term Voltage Issues Caused by Wind Turbines Tripping," Energies, MDPI, vol. 11(7), pages 1-15, July.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:7:p:1709-:d:155512
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    References listed on IDEAS

    as
    1. Tao Ding & Qinglai Guo & Rui Bo & Hongbin Sun & Boming Zhang, 2014. "A Static Voltage Security Region for Centralized Wind Power Integration—Part I: Concept and Method," Energies, MDPI, vol. 7(1), pages 1-24, January.
    2. Christophe Croux & Catherine Dehon, 2010. "Influence functions of the Spearman and Kendall correlation measures," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 19(4), pages 497-515, November.
    3. Pedro G. Lind & Luis Vera-Tudela & Matthias Wächter & Martin Kühn & Joachim Peinke, 2017. "Normal Behaviour Models for Wind Turbine Vibrations: Comparison of Neural Networks and a Stochastic Approach," Energies, MDPI, vol. 10(12), pages 1-14, November.
    4. Tao Ding & Qinglai Guo & Rui Bo & Hongbin Sun & Boming Zhang & Tian-en Huang, 2014. "A Static Voltage Security Region for Centralized Wind Power Integration—Part II: Applications," Energies, MDPI, vol. 7(1), pages 1-18, January.
    5. Yong Wan & Chenqing Fan & Yongshou Dai & Ligang Li & Weifeng Sun & Peng Zhou & Xiaojun Qu, 2018. "Assessment of the Joint Development Potential of Wave and Wind Energy in the South China Sea," Energies, MDPI, vol. 11(2), pages 1-26, February.
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