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Research Papers

An Adaptive Wind Turbine Controller Considering Both the System Performance and Fatigue Loading

[+] Author and Article Information
Zheren Ma

Department of Mechanical Engineering,
University of Texas,
Austin, TX 78712
e-mail: zhrm@utexas.edu

Mohamed L. Shaltout

Mem. ASME
Department of Mechanical Engineering,
University of Texas,
Austin, TX 78712
e-mail: mshaltout@utexas.edu

Dongmei Chen

Mem. ASME
Department of Mechanical Engineering,
University of Texas,
Austin, TX 78712
e-mail: dmchen@me.utexas.edu

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received July 25, 2014; final manuscript received June 24, 2015; published online August 14, 2015. Assoc. Editor: Bryan Rasmussen.

J. Dyn. Sys., Meas., Control 137(11), 111007 (Aug 14, 2015) (10 pages) Paper No: DS-14-1301; doi: 10.1115/1.4031045 History: Received July 25, 2014

Wind energy is a clean and renewable source for electricity generation. To reduce the costs associated with wind power generation, development of a control methodology that maximizes the wind energy capture and mitigates the turbine fatigue loading is desired. In this paper, a new adaptive gain modified optimal torque controller (AGMOTC) for wind turbine partial load operation is presented. A gain-scheduling technique with an internal proportional integral (PI) control is developed to accelerate the controller's convergence to a reference tip speed ratio (TSR). The reference TSR is then adjusted to its optimal value in real-time through an adaptive algorithm capable of rejecting model uncertainties and estimation errors of the control gain. A fatigue mitigation method is also designed to reduce the impact of exacerbated tower bending moments due to the resonance effect. The proposed AGMOTC is evaluated based on the National Renewable Energy Laboratory (NREL) 5 MW wind turbine model using the NREL fast simulator. Simulation results have shown that the AGMOTC has improved efficiency and robustness in wind energy capture and reduced tower fatigue loading as compared to the traditional control technique.

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References

Figures

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Fig. 1

Power coefficient versus TSR and blade pitch angle

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Fig. 2

Thrust coefficient versus TSR and blade pitch angle

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Fig. 3

A simplified block diagram of a wind turbine system with the AGMOTC controller

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Fig. 4

Reference power coefficient curve with respect to TSR and the corresponding cubic function of TSR

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Fig. 5

Performance comparison between the STC and AGMOTC without considering prediction error in KSTC for wind class 4

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Fig. 6

Fatigue loading comparison between the STC and AGMOTC without considering prediction error in KSTC for wind class 4

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Fig. 7

Performance comparison between the STC and AGMOTC considering prediction error in KSTC for wind class 4

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Fig. 8

Fatigue loading comparison between the STC and AGMOTC considering prediction error in KSTC for wind class 4

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Fig. 9

The effect of fatigue mitigation approach on the rotor speed, generator power, and TSSM for wind class 4

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Fig. 10

The effect of fatigue mitigation approach on the FFT of the TSSM for wind class 4

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