Application of Soft Computing in the Design and Optimization of Tuned Liquid Column–Gas Damper for Use in Offshore Wind Turbines

Reza Dezvareh
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引用次数: 3

Abstract

Article History: Received: 15 Feb. 2019 Accepted: 18 Mar. 2019 Tuned liquid column gas damper is a new type of energy absorber that can mitigate the vibrations of structures if their frequency and mass parameters are well tuned. Since this damper has recently been introduced and its behaviour in certain structures such as offshore oil platforms and wind turbines has already been tested, a suitable and accurate method is required to identify these optimal parameters. Therefore, considering the complexity of loads exerted on wind turbines in seas (wave and wind loads), in present study attempts are made to use a new artificial neural network approach to obtain optimal tuned liquid column–gas damper (TLCGD) parameters for mitigation of wind turbine vibrations. First fixed offshore wind turbines at various depths are designed in the MATLAB coding environment. After obtaining the stiffness, damping and mass matrices of the structures, the program enters the Simulink, and the wind turbine structure along with the TLCGD is exposed to different wave-wind load combinations within reasonable range of damper parameters. The neural network training is launched based on available statistical data of the offshore wind turbine with different heights as well as different frequency and mass ratios of the damper. According to this method, the percentage of errors found in the neural network outputs was negligible compared to the actual results obtained from the analysis in Simulink (even for inputs that stood outside the training range of the neural network). The mean error percentage, the standard deviation and the effective value of the neural network with actual values are below 10% for all three types of the structure. Finally, the method presented in this study can be used to obtain optimal parameters of the TLCGD for all kinds of offshore wind turbines at different depths of the sea, which leads to the optimal design of this damper to reduce the vibrations of wind turbines under wave and wind load pressures.
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软计算在海上风力机液柱-气调谐阻尼器设计与优化中的应用
调谐液柱气体减振器是一种新型的减振器,通过对结构的频率和质量参数进行良好的调谐,可以减轻结构的振动。由于这种阻尼器是最近才引入的,并且它在某些结构(如海上石油平台和风力涡轮机)中的性能已经经过测试,因此需要一种合适而准确的方法来确定这些最佳参数。因此,考虑到风力机在海上所受载荷的复杂性(波浪和风荷载),本研究尝试使用一种新的人工神经网络方法来获得用于风力机振动的最佳调谐液柱-气阻尼器(TLCGD)参数。首先在MATLAB编码环境下设计了不同深度的固定式海上风力发电机。在获得结构刚度矩阵、阻尼矩阵和质量矩阵后,程序进入Simulink,在合理的阻尼器参数范围内,将风力机结构与TLCGD一起暴露在不同的波风荷载组合下。神经网络训练是基于现有海上风力机不同高度、不同阻尼器频率和质量比的统计数据进行的。根据这种方法,与在Simulink中分析获得的实际结果相比,在神经网络输出中发现的误差百分比可以忽略不计(即使是在神经网络的训练范围之外的输入)。对于这三种结构,神经网络的平均误差百分比、标准差和有效值与实际值均在10%以下。最后,利用本文提出的方法可获得不同水深下各类海上风力机的TLCGD最优参数,从而对该阻尼器进行优化设计,以减小风力机在波浪和风荷载压力下的振动。
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