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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, Open Access Journal, 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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    More about this item

    Keywords

    wind turbine; wind parameter; measurement awareness; predictive perception; recombination sensing; technology framework;

    JEL classification:

    • Q - Agricultural and Natural Resource Economics; Environmental and Ecological Economics
    • Q0 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • Q49 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Other

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