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Intelligent robust pitch control of wind turbine using brain emotional learning
International Transactions on Electrical Energy Systems ( IF 1.9 ) Pub Date : 2021-01-04 , DOI: 10.1002/2050-7038.12785
Zhi Cao 1 , Amirmehdi Yazdani 2 , Amin Mahmoudi 1
Affiliation  

This article proposes an implementation of the brain emotional learning‐based intelligent controller (BELBIC) for high‐precision and robust pitch control of a 5‐MW wind turbine. The proposed model‐free controller is a biologically inspired method emulating the learning in the mammalian's limbic system and it is independent of the model dynamics and variations that might occur in a system. The auto‐learning capability of the BELBIC allows accommodating the nonlinearities associated with the wind turbine model and provides a reasonable degree of disturbance enabling precise and robust tracking of the pitch angle, even under unforeseen wind conditions. To investigate the trajectory tracking performance and robustness of the BELBIC in various unpredictable wind conditions, multiple uncertain wind speed conditions including gust and random wind, are simulated in MATLAB/Simulink. The results of simulations are compared with two benchmark control methods, fuzzy‐proportional‐integral‐derivative and gain‐scheduling proportional‐integral. The simulation results clearly indicate that the BELBIC serves better performance and robustness while guaranteeing quick and precise pitch angle response as well as its ability in dealing with nonlinearity and unforeseen wind conditions in comparison to the other two benchmark control methods.

中文翻译:

基于大脑情感学习的风力发电机智能鲁棒桨距控制

本文提出了一种基于大脑情感学习的智能控制器(BELBIC)的实现,该控制器可用于5兆瓦风力发电机的高精度和鲁棒的俯仰控制。所提出的无模型控制器是一种生物学启发的方法,可以模拟哺乳动物边缘系统中的学习,并且与模型动力学和系统中可能发生的变化无关。BELBIC的自动学习功能允许适应与风力涡轮机模型相关的非线性,并提供合理程度的扰动,即使在不可预见的风力条件下也能精确且可靠地跟踪俯仰角。为了研究BELBIC在各种不可预测的风况,多种不确定风速条件(包括阵风和随机风)下的轨迹跟踪性能和鲁棒性,在MATLAB / Simulink中进行了仿真。仿真结果与两种基准控制方法进行了比较:模糊比例积分微分和增益调度比例积分。仿真结果清楚地表明,与其他两种基准控制方法相比,BELBIC具有更好的性能和鲁棒性,同时保证了快速,精确的俯仰角响应以及其处理非线性和不可预见的风况的能力。
更新日期:2021-03-02
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