Model free adaptive control of large and flexible wind turbine rotors with controllable flaps

Author(s):  
Juan Li ◽  
Yinan Wang ◽  
Xiaowei Zhao ◽  
Pengyuan Qi
2021 ◽  
Author(s):  
Daniel Escobar-Naranjo ◽  
Biswaranjan Mohanty ◽  
Kim A. Stelson

Abstract Adaptive control strategies are commonly used for systems that change over time, such as wind turbines. Extremum Seeking Control (ESC) is a model-free real-time adaptive control strategy commonly used in conventional gearbox wind turbines for Maximum Power Point Tracking (MPPT). ESC optimizes the rotor power by constantly tuning the torque control gain (k) when operating below rated power. The same concept can be applied for hydrostatic wind turbines. This paper studies the use of ESC for a 60-kW hydrostatic wind turbine. First, a systematic approach to establish the ideal ESC is shown. Second, a comparison of the power capture performance of ESC versus the conventional torque control law (the kω2 law) is shown. The simulations include a timesharing power capture coefficient (Cp) to clearly show the advantages of using ESC. Studies under steady and realistic wind conditions show the main advantages of using ESC for a hydrostatic wind turbine.


Author(s):  
Yao Wenlong ◽  
Qi Guanhua ◽  
Yang ke ◽  
Chi Ronghu ◽  
Yang Dejing

Author(s):  
Na Dong ◽  
Wenjin Lv ◽  
Shuo Zhu ◽  
Donghui Li

Model-free adaptive control has been developed greatly since it was proposed. Up to now, model-free adaptive control theory has become mature and tends to be an effective solution for complex unmodeled industrial systems. In practical industrial processes, most control systems are inevitably accompanied by noise that will result in indelible error and may further cause inaccurate feedback to the output. In order to solve this kind of problem with model-free technique, this article incorporates an improved tracking differentiator into model-free adaptive control. After that, the anti-noise model-free adaptive control method with complete convergence analysis is proposed. Meanwhile, numerical simulation proves that the improved control method can quickly track a given signal with good resistance to noise interference. Finally, the effectiveness and practicability of the proposed algorithm are verified by experiments through the control of drum water level of circulating fluidized.


Inventions ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. 3
Author(s):  
Wenping Cao ◽  
Ning Xing ◽  
Yan Wen ◽  
Xiangping Chen ◽  
Dong Wang

Wind energy conversion systems have become a key technology to harvest wind energy worldwide. In permanent magnet synchronous generator-based wind turbine systems, the rotor position is needed for variable speed control and it uses an encoder or a speed sensor. However, these sensors lead to some obstacles, such as additional weight and cost, increased noise, complexity and reliability issues. For these reasons, the development of new sensorless control methods has become critically important for wind turbine generators. This paper aims to develop a new sensorless and adaptive control method for a surface-mounted permanent magnet synchronous generator. The proposed method includes a new model reference adaptive system, which is used to estimate the rotor position and speed as an observer. Adaptive control is implemented in the pulse-width modulated current source converter. In the conventional model reference adaptive system, the proportional-integral controller is used in the adaptation mechanism. Moreover, the proportional-integral controller is generally tuned by the trial and error method, which is tedious and inaccurate. In contrast, the proposed method is based on model predictive control which eliminates the use of speed and position sensors and also improves the performance of model reference adaptive control systems. In this paper, the proposed predictive controller is modelled in MATLAB/SIMULINK and validated experimentally on a 6-kW wind turbine generator. Test results prove the effectiveness of the control strategy in terms of energy efficiency and dynamical adaptation to the wind turbine operational conditions. The experimental results also show that the control method has good dynamic response to parameter variations and external disturbances. Therefore, the developed technique will help increase the uptake of permanent magnet synchronous generators and model predictive control methods in the wind power industry.


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