Comparison of linear and nonlinear model predictive control of wind turbines using LIDAR

Recent developments in remote sensing are offering a promising opportunity to rethink conventional control strategies of wind turbines. With technologies such as LIDAR, the information about the incoming wind field - the main disturbance to the system - can be made available ahead of time. Feedforward control can be easily combined with traditional collective pitch feedback controllers and has been successfully tested on real systems. Nonlinear model predictive controllers adjusting both collective pitch and generator torque can further reduce structural loads in simulations but have higher computational times compared to feedforward or linear model predictive controller. This paper compares a linear and a commercial nonlinear model predictive controller to a baseline controller. On the one hand simulations show that both controller have significant improvements if used along with the preview of the rotor effective wind speed. On the other hand the nonlinear model predictive controller can achieve better results compared to the linear model close to the rated wind speed

ADV preview based nonlinear predictive control for maximizing power generation of a tidal turbine with hydrostatic transmission

As the development of tidal turbines attracts more and more attention in recent years, reliable design and efficient control of tidal turbines are becoming increasingly important. However, the majority of existing tidal turbines still utilize traditional fixed ratio geared transmissions and the associated control designs focus on simple feedback controllers that use measurements or possibly estimates of the turbine itself or current local tidal profile. Therefore, the measurement and control are inevitably affected by the inherent delay with respect to the current tidal speeds. This paper proposes a novel tidal turbine with continuously variable speed hydrostatic transmissions and a nonlinear predictive controller that uses short-term predictions of the approaching tidal speed field to enhance the maximum tidal power generations when the tidal speed is below the rated value. The controller is designed based on an offline finite-horizon continuous time minimization of a cost function, and an integral action is incorporated into the control loop to increase the robustness against parameter variations and uncertainties. A smooth second order sliding mode observer is also designed for parameter estimations in the control loop. A 150 kW tidal turbine with hydrostatic transmission is designed and implemented. The results demonstrate that the averaged generator power increases by 6.76% with this preview based nonlinear predictive controller compared with a classical non-predictive controller

LIDAR-based wind speed modelling and control system design

Abstract—The main objective of this work is to explore the feasibility of using LIght Detection And Ranging (LIDAR) measurement and develop feedforward control strategy to improve wind turbine operation. Firstly the Pseudo LIDAR measurement data is produced using software package GH Bladed across a distance from the turbine to the wind measurement points. Next the transfer function representing the evolution of wind speed is developed. Based on this wind evolution model, a model-inverse feedforward control strategy is employed for the pitch control at above-rated wind conditions, in which LIDAR measured wind speed is fed into the feedforward. Finally the baseline feedback controller is augmented by the developed feedforward control. This control system is developed based on a Supergen 5MW wind turbine model linearised at the operating point, but tested with the nonlinear model of the same system. The system performances with and without the feedforward control channel are compared. Simulation results suggest that with LIDAR information, the added feedforward control has the potential to reduce blade and tower loads in comparison to a baseline feedback control alone

A Doppler Lidar system with preview control for wind turbine load mitigation

This dissertation focuses on the development of a system for wind turbine in order to mitigate the load from unstable wind speed. The work is divided into 2 main parts: a cost efficient Doppler wind Lidar system is developed based on a short coherence length laser system in combine with multiple length delayline concept; a preview pitch control is developed based on the design of a combination of 2 degree of freedom (2-DOF) feedback / feedforward control with a model predictive control

A novel switched model predictive control of wind turbines using artificial neural network-Markov chains prediction with load mitigation

The existing model predictive control algorithm based on continuous control using quadratic programming is currently one of the most used modern control strategies applied to wind turbines. However, heavy computational time involved and complexity in implementation are still obstructions in existing model predictive control algorithm. Owing to this, a new switched model predictive control technique is developed for the control of wind turbines with the ability to reduce complexity while maintaining better efficiency. The proposed technique combines model predictive control operating on finite control set and artificial intelligence with reinforcement techniques (Markov Chains, MC) to design a new effective control law which allows to achieve the control objectives in different wind speed zones with minimization of computational complexity. The proposed method is compared with the existing model predictive control algorithm, and it has been found that the proposed algorithm is better in terms of computational time, load mitigation, and dynamic response. The proposed research is a forward step towards refining modern control techniques to achieve optimization in nonlinear process control using novel hybrid structures based on conventional control laws and artificial intelligence.© 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)fi=vertaisarvioitu|en=peerReviewed