An Adaptive Economic Model Predictive Control Approach for Wind Turbines

[+] Author and Article Information
Mohamed Shaltout

ASME Member, Mechanical Design and Production Department, Faculty of Engineering, Cairo University, Giza 12613, Egypt

Zheren Ma

ASME Member, Department of Mechanical Engineering, University of Texas at Austin, Austin, TX 78712

Dongmei Chen

ASME Member, Department of Mechanical Engineering, University of Texas at Austin, Austin, TX 78712

1Corresponding author.

ASME 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. LIDAR) have paved the way for implementing advanced control algorithms in the wind energy industry. In this paper, a LIDAR-assisted economic model predictive control 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.

Copyright (c) 2017 by ASME
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