[1]王昊,郝万君,郝诗源,等.基于HOSMO的风电机组自适应超扭曲滑模控制[J].深圳大学学报理工版,2020,37(5):507-513.[doi:10.3724/SP.J.1249.2020.05507]
 WANG Hao,HAO Wanjun,HAO Shiyuan,et al.HOSMO based adaptive super twist sliding-mode control for wind turbine[J].Journal of Shenzhen University Science and Engineering,2020,37(5):507-513.[doi:10.3724/SP.J.1249.2020.05507]
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基于HOSMO的风电机组自适应超扭曲滑模控制()
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《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第37卷
期数:
2020年第5期
页码:
507-513
栏目:
环境与能源
出版日期:
2020-09-15

文章信息/Info

Title:
HOSMO based adaptive super twist sliding-mode control for wind turbine
文章编号:
202005008
作者:
王昊1郝万君1郝诗源2曹松青1孙志辉1
1)苏州科技大学电子与信息工程学院,江苏苏州215009
2)丹麦科技大学电气工程系,丹麦哥本哈根999017
Author(s):
WANG Hao1 HAO Wanjun1 HAO Shiyuan2 CAO Songqing1 and SUN Zhihui1
1) School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, Jiangsu Province, P.R.China
2) Department of Electrical Engineering, Danish University of Science and Technology, Copenhagen 999017, Denmark
关键词:
风力发电机组最大功率点跟踪自适应超扭曲滑模高阶滑模观测器鲁棒性
Keywords:
wind turbine maximum power point tracking adaptive super twist sliding mode high order sliding mode observer (HOSMO) robustness
分类号:
TP273
DOI:
10.3724/SP.J.1249.2020.05507
文献标志码:
A
摘要:
为提高风力发电系统在风速随机变化、未知扰动输入的运行环境下的最大功率跟踪控制性能,提出一种基于高阶滑模观测器(high order sliding mode observer, HOSMO)的自适应超扭曲滑模控制策略.为增强风机系统的动态跟踪性能,设计超扭曲滑模控制器.为验证并提高该方法的控制性能,对超扭曲滑模控制器中的未知参数和系统的不确定性设计自适应控制律,更好地抑制了超扭曲控制器的抖振,增强了对模型参数和外部扰动不确定性的鲁棒性.同时针对风机系统动态模型中部分状态变量难以准确测量的问题,设计了HOSMO对其实时估计,该方法对高频噪声有很好的抑制作用.将提出的控制方法与一阶滑模控制以及传统比例积分控制进行对比分析,证明了所提策略的正确性和可行性.
Abstract:
In order to improve the maximum power tracking control performance of wind power system in the operating environment of wind speed random change and unknown disturbance input, we propose an adaptive super twist sliding mode control strategy based on high order sliding mode observer (HOSMO). Aiming at enhancing the dynamic tracking performance of the fan system, we design a super twist sliding mode controller. In order to improve the performance of the method, an adaptive control law is designed for the unknown parameters and system uncertainties in the super twist sliding mode controller, which can better suppress the chattering of the super twist controller and enhance the robustness to the model parameters and external disturbance uncertainties. At the same time, aiming at the problem which is difficult to measure some state variables accurately in the dynamic model of the fan system, we design a high-order sliding mode observer to estimate the real-time state variables, which has a good effect on suppressing the high-frequency noise. The correctness and feasibility of the proposed strategy are proved by comparing the proposed control method with the first-order sliding mode control and the traditional proportional integral control.

参考文献/References:

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备注/Memo

备注/Memo:
Received:2019-10-29;Accepted:2020-01-13
Foundation:National Natural Science Foundation of China (51477109, 61703296); Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX19_2016)
Corresponding author:Professor HAO Wanjun. E-mail: hao_wanjun@163.com
Citation:WANG Hao, HAO Wanjun, HAO Shiyuan, et al. HOSMO based adaptive super twist sliding-mode control for wind turbine[J]. Journal of Shenzhen University Science and Engineering, 2020, 37(5): 507-513.(in Chinese)
基金项目:国家自然科学基金资助项目(51477109,61703296);江苏省研究生科研与实践创新计划资助项目(KYCX19_2016)
作者简介:王昊(1994—),苏州科技大学硕士研究生.研究方向:智能控制算法,风力发电系统建模与控制,新能源发电.E-mail:1947636045@qq.com
引文:王昊,郝万君,郝诗源,等.基于HOSMO的风电机组自适应超扭曲滑模控制[J]. 深圳大学学报理工版,2020,37(5):507-513.
更新日期/Last Update: 2020-07-26