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

Xiaojun Shen, Chongchen Zhou, Guojie Li, Xuejiao Fu and Tek Tjing Lie
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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

Energies, 2018, vol. 11, issue 7, 1-23

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.

Keywords: wind turbine; wind parameter; measurement awareness; predictive perception; recombination sensing; technology framework (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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