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Research Papers

Wind Speed Estimation Algorithm in the Presence of Observation Noise

[+] Author and Article Information
Ken Mok

 Qualcomm Incorporated, 5775 Morehouse Drive, San Diego, CA 92121

J. Sol. Energy Eng 132(1), 011009 (Dec 21, 2009) (6 pages) doi:10.1115/1.4000604 History: Received September 03, 2008; Revised October 19, 2009; Published December 21, 2009

A stochastic hub-height wind speed estimation algorithm for wind turbines, which requires only the rotor angular velocity measurement in the presence of observation noise is proposed. It is assumed that the power curve and the total inertia of the rotor are known a priori. We use the maximum likelihood estimator to identify the hub-height wind speed, which is formulated as one of the state variables in the nonlinear model. The theory and algorithm are verified by both simulated and experimental data in the presence of measurement noise. The effects of the initial guess, the noise variance, and the time history are discussed.

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Figures

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Figure 1

Simulated observation data and the calculated system response

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Figure 2

Percentage estimation error in v

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Figure 3

Percentage estimation error in ω(0)

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Figure 4

Estimation of time-varying wind speed, T=0.5 s

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Figure 5

Estimation of time-varying wind speed, T=0.5 s (close up)

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Figure 6

Estimation of time-varying wind speed, T=0.1 s

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Figure 7

Estimation of time-varying wind speed, T=0.1 s (close up)

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Figure 8

Estimation of time-varying wind speed, T=5 s

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Figure 9

Estimation of time-varying wind speed, T=5 s (close up)

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Figure 10

Estimated (time delayed by 12 s) and measured wind speed from real-time operation of CART

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