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Overview of Wind Parameters Sensing Methods and Framework of a Novel MCSPV Recombination Sensing Method for Wind Turbines

Author

Listed:
  • Xiaojun Shen

    (Department of Electrical Engineering, Tongji University, Shanghai 200092, China)

  • Chongchen Zhou

    (Department of Electrical Engineering, Tongji University, Shanghai 200092, China)

  • Guojie Li

    (Department of Electrical Engineering, Shanghai Jiaotong University, Shanghai 200240, China)

  • Xuejiao Fu

    (Department of Electrical Engineering, Tongji University, Shanghai 200092, China)

  • Tek Tjing Lie

    (Department of Electrical and Electronic Engineering, Auckland University of Technology, 1142 Auckland, New Zealand)

Abstract

The paper presents an overview of the traditional methods to obtain wind parameters such as wind speed, wind direction and air density. After analyzing wind turbines’ arrangements and communication characteristics and the correlation of operation data between wind turbines, the paper proposes a novel recombination-sensing method route of “measuring–correlating–sharing–predicting–verifying” (MCSPV) and explores its feasibility. The analysis undertaken in the paper shows that the wind speed and wind direction instrument fixed on the wind turbine nacelle is simple and economical. However, it performs in-process measurement, which restricts the control optimization of wind turbines. The light detection and ranging (LIDAR) technology which is accurate and fast, ensures an early and super short-time sensing of wind speed and wind direction but it is costly. The wind parameter predictive perception method can predict wind speed and wind power at multiple time scales statistically, but it has limited significance for the control of the action of wind turbines. None of the traditional wind parameter-sensing methods have ever succeeded in air density sensing. The MCSPV recombination sensing method is feasible, both theoretically and in engineering, for realizing the efficient and accurate sensing and obtaining of such parameters as wind speed, wind direction and air density aimed at the control of wind turbines.

Suggested Citation

  • Xiaojun Shen & Chongchen Zhou & Guojie Li & Xuejiao Fu & Tek Tjing Lie, 2018. "Overview of Wind Parameters Sensing Methods and Framework of a Novel MCSPV Recombination Sensing Method for Wind Turbines," Energies, MDPI, vol. 11(7), pages 1-23, July.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:7:p:1747-:d:156011
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    References listed on IDEAS

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    1. Xiaojun Shen & Chongcheng Zhou & Xuejiao Fu, 2018. "Study of Time and Meteorological Characteristics of Wind Speed Correlation in Flat Terrains Based on Operation Data," Energies, MDPI, vol. 11(1), pages 1-16, January.
    2. Boutoubat, M. & Mokrani, L. & Machmoum, M., 2013. "Control of a wind energy conversion system equipped by a DFIG for active power generation and power quality improvement," Renewable Energy, Elsevier, vol. 50(C), pages 378-386.
    3. Bottasso, C.L. & Pizzinelli, P. & Riboldi, C.E.D. & Tasca, L., 2014. "LiDAR-enabled model predictive control of wind turbines with real-time capabilities," Renewable Energy, Elsevier, vol. 71(C), pages 442-452.
    4. Liu, Da & Niu, Dongxiao & Wang, Hui & Fan, Leilei, 2014. "Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm," Renewable Energy, Elsevier, vol. 62(C), pages 592-597.
    5. Kusiak, Andrew & Li, Wenyan, 2010. "Short-term prediction of wind power with a clustering approach," Renewable Energy, Elsevier, vol. 35(10), pages 2362-2369.
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    Cited by:

    1. Dongheon Shin & Kyungnam Ko, 2019. "Application of the Nacelle Transfer Function by a Nacelle-Mounted Light Detection and Ranging System to Wind Turbine Power Performance Measurement," Energies, MDPI, vol. 12(6), pages 1-15, March.

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