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Energies 2017, 10(12), 2145;

Adaptive State Feedback—Theory and Application for Wind Turbine Control

Aerospace Mechanics Division, University of Dayton Research Institute, 300 College Park, Dayton, OH 45469, USA
Mechanical, Aerospace and Biomedical Engineering, University of Tennessee Space Institute, Tullahoma, TN 37388, USA
NASA Ames Research Center, Mofett Field, CA 95034, USA
School of Hydraulic, Energy and Power Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China
Author to whom correspondence should be addressed.
Received: 1 October 2017 / Revised: 27 November 2017 / Accepted: 11 December 2017 / Published: 15 December 2017
(This article belongs to the Special Issue Wind Generators Modelling and Control)
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A class of adaptive disturbance tracking controllers (ADTCs) is augmented with disturbance and state estimation and adaptive state feedback, in which a controller and estimator, which are designed on the basis of a lower-order model, are used to control a higher-order nonlinear plant. The ADTC requires that the plant be almost strict positive real (ASPR) to ensure stability. In this paper, we show that the ASPR property of a plant is retained with the addition of disturbance and state estimation and state feedback, thereby ensuring the stability of the augmented system. The proposed adaptive controller with augmentation is presented in the context of maximum power extraction from a wind turbine in a low-wind-speed operation region. A simulation and comparative study on the National Renewable Energy Laboratory’s (NREL’s) 5 MW nonlinear wind turbine model with an existing baseline Proportional-Integral-Derivative(PID) controller shows that the proposed controller is more effective than the existing baseline PID controller. View Full-Text
Keywords: wind energy; adaptive control; wind turbine control; maximum power point tracking wind energy; adaptive control; wind turbine control; maximum power point tracking

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Thapa Magar, K.; Balas, M.; Frost, S.; Li, N. Adaptive State Feedback—Theory and Application for Wind Turbine Control. Energies 2017, 10, 2145.

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