基于散射中心的风电机叶片散射电场求解
Scattering Electric Field Solution of Wind Turbine Blade Based on Scattering Center
  
DOI:
中文关键词:  风电机叶片,散射电场,散射中心,雷达逆合成孔径图像,CLEAN 算法,快速求解
英文关键词:wind turbine blade, scattering electric field, scattering center, Inverse Synthetic Aperture Radar (ISAR) image, CLEAN algorithm, fast solution
基金项目:国家自然科学基金(51977121)
作者单位
唐 波1,2刘 钢1蔡智勇3谢黄海1 1. 三峡大学 电气与新能源学院,宜昌 443002
2. 三峡大学 湖北省输电线路工程技术研究中心,宜昌 443002
3. 福建智达力胜电子科技有限公司,福州 350019 
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中文摘要:
      为降低雷达系统的数据处理量及实现风电机实时目标特征识别,必须寻求风电机叶片散射电场的快速求解方法。针对传统电磁求解算法需处理巨量风电机叶片散射电场数据的问题,突破传统以超大电尺寸目标实体为基本单元进行广域空间电磁散射积分计算的思想,提出了一种基于散射中心的风电机叶片散射电场求解方法。基于非衰减指数和散射中心模型,通过距离-多普勒算法构建风电机叶片的雷达逆合成孔径图像,采用CLEAN 算法并结合相关系数提取风电机叶片的散射中心参数集,最终以散射中心产生的散射电场数据等效替代叶片本体的全部散射数据,实现了叶片散射电场的快速求解。以Vestas V82-1.65MW 型风电机为例,以矩量法计算结果为基准,文中所提方法计算准确度为93.20%,数据的压缩比达76.81,计算量的数量级比矩量法降低了106。
英文摘要:
      In order to reduce the data processing of radar system and realize the real-time target recognition of wind turbines, it is necessary to find a fast solution to the scattering electric field of wind turbine blades. Aiming at the problem that the traditional electromagnetic solution algorithm needs to deal with a large amount of scattering electric field data of wind turbine blades, this paper breaks through the traditional idea of calculating electromagnetic scattering integrals in a wide area with a super-large-sized target entity as the basic unit, a method for solving the scattering electric field of a wind turbine is proposed. Based on the Undamped Exponentials (UDE) scattering center model, the Inverse Synthetic Aperture Radar (ISAR) image of wind turbine blade is constructed by Range-Doppler (R-D) algorithm. Then, using CLEAN algorithm and correlation coefficient to extract scattering center parameter set of wind turbine blade. Finally, the scattering electric field data of the scattering center is used to replace all the scattering data of the blade, and the fast solution of the scattering electric field is realized. Taking Vestas V82-1. 65MW wind turbine as an example, based on the results of the moment method, the accuracy of the proposed method is 93. 20%, the compression ratio of data is 76. 81, and the calculation amount is 106 times lower than that of the moment method.
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