In this paper, a model predictive control (MPC) approach is presented to maximize the energy generated by a small vertical axis wind turbine (VAWT) subject to current and voltage constraints of electrical and power electronic components. Our method manipulates a load coefficient and optimizes the control trajectory over a prediction horizon such that a cost function that measures the deviation from the maximum available energy and the violation of current and voltage constraints is minimized. Simplified models for the VAWT and a permanent magnet generator have been used. A number of simulations have been carried out to demonstrate the performance of the proposed method at step and oscillatory wind conditions. Furthermore, impacts of the constraints on energy generation have been investigated. Moreover, the performance of the MPC has been compared with a typical maximum power point tracking algorithm in order to show that maximizing the instantaneous power does not mean maximizing the energy; and simulation results have shown that the MPC outperforms the maximum power point tracking algorithm in terms of generated energy by allowing deviations from the maximum power instantaneously for future gains in energy generation.
- Dynamic Systems and Control Division
Model Predictive Control for Energy Maximization of Small Vertical Axis Wind Turbines
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Onol, AO, Sancar, U, Onat, A, & Yesilyurt, S. "Model Predictive Control for Energy Maximization of Small Vertical Axis Wind Turbines." Proceedings of the ASME 2015 Dynamic Systems and Control Conference. Volume 1: Adaptive and Intelligent Systems Control; Advances in Control Design Methods; Advances in Non-Linear and Optimal Control; Advances in Robotics; Advances in Wind Energy Systems; Aerospace Applications; Aerospace Power Optimization; Assistive Robotics; Automotive 2: Hybrid Electric Vehicles; Automotive 3: Internal Combustion Engines; Automotive Engine Control; Battery Management; Bio Engineering Applications; Biomed and Neural Systems; Connected Vehicles; Control of Robotic Systems. Columbus, Ohio, USA. October 28–30, 2015. V001T05A003. ASME. https://doi.org/10.1115/DSCC2015-9891
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