利用调频质量阻尼器结构实现海上漂浮式风力机的稳定性控制

来源 :中国电机工程学报 | 被引量 : 0次 | 上传用户:chenchendewei
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海上漂浮式风力机是在随机性风载荷和浪载荷联合作用下工作的,其结构动态稳定性分析与控制是亟待解决的关键问题之一。考虑到气动水动结构耦合作用而产生6种摇荡形式,该文选择Spar结构的漂浮风力机作为样机系统。针对其纵摇和垂荡这2种摇荡现象,提出了以调频质量阻尼器(tuned mass damper,TMD)置于风力机机舱的结构主动控制形式,建立了基于TMD控制的精确三自由度漂浮风力机动力学模型,其中外激励风载荷考虑了桨叶和塔架2种载荷,而浪载荷按Morison方程建模。基于此模型,分析了随机风浪载荷作用下对风力机性能影响最大的纵摇形式的动态响应,实现了以最小能量消耗来实现风力机塔顶位移、速度、加速度最小为控制目标的线性二次型调节器(linear quadratic regulator,LQR)的设计。以5MW漂浮风力机为例,进行了控制前后的纵摇动态响应仿真和分析。仿真结果表明:通过TMD主动控制,可以有效减少55%塔架振动的最大位移,并快速消弱塔架振动回归稳定状态。 Floating wind turbines at sea are operated under the combined effect of stochastic wind load and wave load. The analysis and control of the dynamic stability of the structure is one of the key problems to be solved urgently. Taking into account the coupling effect of aerodynamic and hydrodynamic structure, six kinds of rocking modes are generated. In this paper, the Spar-structure floating wind turbine is chosen as the prototype system. Aiming at the two swaying phenomena of pitching and heaving, this paper proposes a structure active control mode of tuned mass damper (TMD) placed in the wind turbine engine compartment, and establishes an accurate three degrees of freedom floating wind turbine based on TMD control The dynamic model, in which the external excitation wind load takes into account two loads of blade and tower, and the wave load is modeled by the Morison equation. Based on this model, the dynamic response of pitch mode with the strongest influence on wind turbine performance under stochastic wind and wave loads is analyzed, and the linear quadratic control with minimum displacement, velocity and acceleration of wind turbine overhead is achieved Linear quadratic regulator (LQR) design. Take 5 MW floating wind turbine as an example, the simulation and analysis of pitch dynamic response before and after control are carried out. The simulation results show that the TMD active control can effectively reduce the maximum displacement of 55% of tower vibration and quickly weaken the stability of tower vibration regression.
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