Dynamic RCS Statistical Model of Wind Turbine Blades Driven by Knowledge and Data
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摘要: 针对风力发电场对雷达等设备影响评估中所需风力发电机动态雷达散射截面(RCS)估计的问题，提出了一种知识与数据联合驱动的风力发电机动态RCS统计模型。首先，利用风力发电机叶片RCS随叶片旋转周期性变化的特点，建立叶片RCS单个单调变化区间内的变化函数。该变化函数由与叶片几何参数相关的峰值RCS、与叶片几何参数无关的调制函数、与材质和形状细节相关的乘性因子组成。其中峰值RCS由理论模型推算得到，针对RCS变化复杂的特点，调制函数和乘性因子利用实测训练数据估计得到。其次，对于待求解型号的风力发电机，根据风力发电机几何参数得到其叶片RCS变化函数，再通过参数估计的方法计算其概率密度函数统计模型。多种不同型号风力发电机实测数据的实验结果，验证了该文给出的风力发电机叶片动态RCS统计模型，与实测数据结果有良好的一致性。Abstract: To address the problem of dynamic Radar Cross Section (RCS) estimation of a wind turbine when assessing the wind farm impact on radars and other equipment, a dynamic RCS statistical model of wind turbines driven by knowledge and data is proposed. First, the blades’ RCS variation function in a monotonic variation interval is established using the periodic variation characteristics of the wind turbine blades’ RCS with the blade rotation. The variation function comprises a peak RCS related to the blade geometry, a modulation function independent of the blade geometry and a multiplicative factor related to material and shape details. The peak RCS is calculated from the theoretical model. Furthermore, the modulation function and the multiplicative factor are estimated using the practically measured training data because of the complex characteristics of the RCS variation. Second, for the estimation of a wind turbine statistical model, the blades’ RCS variation function is obtained according to the geometric parameters. Then the statistical model of the probability density function is calculated using the parameter estimation method. The experimental results of the practically measured data of various types of wind turbines verify the dynamic RCS statistical model of the wind turbine blades proposed in this paper, and the model is in good agreement with the practically measured data results.
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表 1 风电场与风机叶片参数
雷达编号 雷达所在位置 风电场名称 风机品牌 风机型号 数量 根部半径 /最大弦长(m) 叶长(m) KDDC Dodge City Gray Country Vestas V47 170 0.65 / 2.5 24.0 Cimarron II Siemens SWT-2.3-93 53 1.00 / 3.6 46.0 Ensign Siemens SWT-2.3-108 43 1.20 / 4.0 54.0 KCRP Corpus Christi Midway Siemens-Gamesa SG 3.4-132 152 1.45 / 4.3 64.5 
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表 2 基于极大似然的分布参数估计(实测数据直接估计/本文方法)
风机 平行状态 垂直状态 尺度参数 形状参数 尺度参数 形状参数 V47 0.5250 / 0.4051 1.9293 / 1.6907 1.9696 / 2.6098 1.0303 / 1.2508 SWT-2.3-93 1.2416 / 0.9631 2.0028 / 1.6907 5.1775 / 6.0026 1.0736 / 1.2508 SWT-2.3-108 1.3098 / 1.2385 1.6559 / 1.6907 7.3717 / 7.4276 1.1714 / 1.2508 SG 3.4-132 1.6889 / 1.6261 1.7093 / 1.6907 8.1494 / 9.1986 1.1052 / 1.2508
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表 3 估计模型与实测数据概率分布函数的差异
风机 平行状态 垂直状态 ${\bar {\varDelta}} _i $ $ \max {\varDelta _i} $ ${ {\bar {\varDelta} } _i}$ $ \max {\varDelta _i} $ V47 0.0458 0.1089 0.0970 0.1725 SWT-2.3-93 0.0415 0.1150 0.0496 0.1038 SWT-2.3-108 0.0174 0.0480 0.0272 0.0692 SG 3.4-132 0.0252 0.0683 0.0340 0.0925
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表 4 本文模型、tLocScale(I&Q)模型与实测数据95%分位点对比(dBsm)
风机 平行状态 垂直状态 实测 tLocScale(I&Q) 本文模型 实测 tLocScale(I&Q) 本文模型 V47 7.2846 0.0606 7.9218 15.4543 10.4123 16.3754 SWT-2.3-93 14.2274 9.5221 15.4477 22.9963 22.0246 23.6108 SWT-2.3-108 15.5344 11.0266 17.6311 25.1997 24.3402 25.4613 SG 3.4-132 17.4104 14.0950 19.9966 26.8829 26.3642 27.3185
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