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Research Papers

An Adaptive Economic Model Predictive Control Approach for Wind Turbines

[+] Author and Article Information
Mohamed L. Shaltout

Mem. ASME
Faculty of Engineering,
Mechanical Design and Production Department,
Cairo University,
Giza 12613, Egypt
e-mail: mshaltout@cu.edu.eg

Zheren Ma

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

Dongmei Chen

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

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received February 16, 2017; final manuscript received November 9, 2017; published online December 19, 2017. Assoc. Editor: Ryozo Nagamune.

J. Dyn. Sys., Meas., Control 140(5), 051007 (Dec 19, 2017) (10 pages) Paper No: DS-17-1098; doi: 10.1115/1.4038490 History: Received February 16, 2017; Revised November 09, 2017

Motivated by the reduction of overall wind power cost, considerable research effort has been focused on enhancing both efficiency and reliability of wind turbines. Maximizing wind energy capture while mitigating fatigue loads has been one of the main goals for control design. Recent developments in remote wind speed measurement systems (e.g., light detection and ranging (LIDAR)) have paved the way for implementing advanced control algorithms in the wind energy industry. In this paper, an LIDAR-assisted economic model predictive control (MPC) framework with a real-time adaptive approach is presented to achieve the aforementioned goal. First, the formulation of a convex optimal control problem is introduced, with linear dynamics and convex constraints that can be solved globally. Then, an adaptive approach is proposed to reject the effects of model-plant mismatches. The performance of the developed control algorithm is compared to that of a standard wind turbine controller, which is widely used as a benchmark for evaluating new control designs. Simulation results show that the developed controller can reduce the tower fatigue load with minimal impact on energy capture. For model-plant mismatches, the adaptive controller can drive the wind turbine to its optimal operating conditions while satisfying the optimal control objectives.

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Figures

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Fig. 1

Power coefficient of the NREL 5 MW horizontal axis wind turbine

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Fig. 2

Thrust coefficient of the NREL 5 MW horizontal axis wind turbine

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Fig. 3

The available power Pav(v,K) normalized by v3 and plotted against kinetic energy K for a range of wind speeds from 3m/s to 25 m/s with an increment of 1 m/s

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Fig. 4

A simplified block diagram of the wind turbine closed-loop system with the eMPC and the adaptive algorithm

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Fig. 9

Theoretical versus deviated power coefficient for the NREL 5 MW horizontal axis wind turbine

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Fig. 5

A comparison between the responses of the BLC and the eMPC to steps in wind speed ranging from 8 to 10 m/s with 1 m/s increment

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Fig. 6

A comparison between the responses of the BLC and the eMPC to steps in wind speed ranging from 11 to 13 m/s with 1 m/s increment

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Fig. 7

A comparison between the responses of the BLC and the eMPC under a 10 min volatile wind profile with an average equal to 7.5 m/s

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Fig. 8

A comparison between the responses of the BLC and the eMPC under a 10 min volatile wind profile with an average equal to 12.5 m/s

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Fig. 10

A comparison between the responses of BLC and eMPC with model-plant mismatches under a 20 min volatile wind profile with an average equal to 7.5 m/s

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