Dynamic Modeling and Control for an Offshore Semisubmersible Floating Wind Turbine

IF 6.4 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS IEEE Transactions on Automation Science and Engineering Pub Date : 2025-02-20 DOI:10.1109/TASE.2025.3541730
Yingjie Gong;Qinmin Yang;Hua Geng;Wenchao Meng;Lin Wang
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Abstract

Floating wind turbines (FWTs) hold significant potential for the exploitation of offshore renewable energy resources. Nevertheless, prior to the construction of FWTs, it is imperative to tackle several critical challenges, especially the issue of performance degradation under combined wind and wave loads. This study initiates with the development of a simplified nonlinear dynamical model for a semi-submersible FWT. In particular, both the rotor dynamics and the finite rotations of the platform are considered in presented modeling approach, thereby effectively capturing the complex interplay between the platform, tower, nacelle, and rotor under combined wind and wave loads. Subsequently, based on the developed FWT model, a novel adaptive nonlinear pitch controller is formulated with the goal of striking a trade-off between regulating power generation and reducing platform motion. Notably, the proposed control strategy adopts a continuous control approach, strategically beneficial in circumventing the chattering phenomenon commonly associated with sliding mode control. Furthermore, the controller integrates an online approximator and a robust integral of the sign of the tracking error, facilitating real-time learning of system unknown dynamics while compensating for bounded disturbances. Finally, both the accuracy of the established nonlinear FWT model in predicting key dynamics and the superiority of the presented pitch controller are validated through comprehensive comparative studies. Note to Practitioners—This paper addresses the conflicting goals between power regulation and load mitigation for floating wind turbines (FWTs) to ensure the reliable operation of wind turbine systems. This remains an ongoing challenge due to the inherent complexity of existing FWT models, frequently resulting in controllers crafted using linearized representations that fail to accommodate real-world uncertainties effectively. Through the utilization of a simplified physical-based nonlinear FWT model, a novel adaptive nonlinear pitch controller emerges as a promising solution. Notably, the developed nonlinear FWT model elucidates the coupling between rotor and platform degrees of freedom clearly and succinctly, facilitating the design of intelligent controllers. Our approach demonstrates the capability to concurrently regulate power production and stabilize the platform. Additionally, an online approximator is integrated into the controller to capture system dynamics, thus augmenting adaptability and diminishing reliance on high-gain feedback compensation. Importantly, this control strategy holds promise for extension and implementation in various other renewable energy systems.
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海上半潜式浮式风力发电机组动力学建模与控制
浮动式风力涡轮机(FWTs)在开发海上可再生能源方面具有巨大的潜力。然而,在建造fwt之前,必须解决几个关键的挑战,特别是在风浪联合载荷下性能下降的问题。本研究从建立半潜式FWT的简化非线性动力学模型开始。特别是,该建模方法同时考虑了转子动力学和平台的有限旋转,从而有效地捕捉了风浪联合载荷下平台、塔、机舱和转子之间的复杂相互作用。随后,基于所建立的FWT模型,设计了一种新的自适应非线性螺距控制器,目的是在调节发电和减少平台运动之间取得平衡。值得注意的是,所提出的控制策略采用连续控制方法,在策略上有利于规避滑模控制中常见的抖振现象。此外,控制器集成了在线逼近器和跟踪误差符号的鲁棒积分,便于实时学习系统未知动态,同时补偿有界干扰。最后,通过全面的对比研究,验证了所建立的非线性FWT模型在预测键动力学方面的准确性和所提出的螺距控制器的优越性。从业人员注意:本文解决了浮动风力涡轮机(FWTs)的功率调节和负荷缓解之间的冲突目标,以确保风力涡轮机系统的可靠运行。由于现有FWT模型固有的复杂性,这仍然是一个持续的挑战,经常导致使用线性化表示的控制器无法有效地适应现实世界的不确定性。通过利用简化的基于物理的非线性FWT模型,一种新的自适应非线性螺距控制器成为一种很有前途的解决方案。值得注意的是,所建立的非线性FWT模型清晰、简洁地阐述了转子与平台自由度之间的耦合关系,便于智能控制器的设计。我们的方法证明了同时调节电力生产和稳定平台的能力。此外,在线逼近器集成到控制器中以捕获系统动态,从而增强适应性并减少对高增益反馈补偿的依赖。重要的是,这种控制策略有望在各种其他可再生能源系统中推广和实施。
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来源期刊
IEEE Transactions on Automation Science and Engineering
IEEE Transactions on Automation Science and Engineering 工程技术-自动化与控制系统
CiteScore
12.50
自引率
14.30%
发文量
404
审稿时长
3.0 months
期刊介绍: The IEEE Transactions on Automation Science and Engineering (T-ASE) publishes fundamental papers on Automation, emphasizing scientific results that advance efficiency, quality, productivity, and reliability. T-ASE encourages interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, operations research, and other fields. T-ASE welcomes results relevant to industries such as agriculture, biotechnology, healthcare, home automation, maintenance, manufacturing, pharmaceuticals, retail, security, service, supply chains, and transportation. T-ASE addresses a research community willing to integrate knowledge across disciplines and industries. For this purpose, each paper includes a Note to Practitioners that summarizes how its results can be applied or how they might be extended to apply in practice.
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