大型风电叶片动态摄影测量网络优化

在大型风电叶片动态摄影测量中，为了对相机的站位进行优化，采用一种变异操作改进型遗传算法作为摄影测量网络优化方法，通过光线束前方交会的误差传递建立测量误差模型，以空间坐标测量误差的标准差为网络优化的目标，同时根据被测风电叶片几何结构和实际环境确定了相应的约束条件进行仿真实验，得到了最优的相机站位。结果表明，在以叶片长度为40m的风机为被测物的仿真实验中，最优站位的空间坐标测量误差标准差为2.7mm；通过对叶片长度为3.5m的风机模型进行实测实验验证，最优站位的相对测量误差为0.009%，最大误差为0.617mm。该研究为风电叶片摄影测量的网络优化提供了参考。

Dynamic photogrammetry network optimization for large wind turbine blades

In order to optimize the camera stations in dynamic photogrammetry for large wind turbine blades, an optimization method of photogrammetric network based on improved genetic algorithm for mutation operation was used. A measurement error model was established based on error propagation in the 3-D reconstruction process by front intersecting ray bundles. Taking the standard deviations of the spatial coordinate measurement error as the goal of network optimization, while considering the constraint conditions caused by the wind turbine blade geometry and the actual environment, a simulation experiment was performed to obtain the optimal camera stations. The results show that, in the simulation experiment, the wind turbine with blade length of 40m was taken as the measuring object, the standard deviation of the spatial coordinate measurement errors of the optimal stations is 2.7mm. Real data experiments are conducted on a wind turbine model with 3.5m blade length. The relative measurement error of the optimal station is 0.009%, and the maximum error is 0.617mm. This study provides reference for the network optimization of photogrammetry of wind turbine blades.