提出一种可用于半潜风电平台主尺度参数的智能优化方法。首先确定了4个重要主尺度参数表达平台设计,形成120组参数组合,并基于OpenFAST与AQWA对所有参数组合的平台进行数值模拟,预报运动响应的短期极值,形成神经网络训练数据库。其次,完成BP神经网络模型的训练,使其对垂荡、纵摇、艏摇以及机舱加速度的短期极值预报误差小于10%,形成代理模型。最后,应用遗传算法对平台垂荡运动响应进行优化,获得的最优平台方案垂荡响应极值,与数据库中存在的最低值相比,降低了6.98%。
Abstract
In this paper, four important main scale parameters are determined to express the platform design, and 120 sets of parameter combinations are formed. Based on OpenFAST and AQWA, the numerical simulation of the platform with all parameter combinations is carried out, and the short-term extreme value of motion response is predicted, forming a neural network training database. Secondly, the training of BP neural network model is completed, so that the short-term extreme prediction error of heave, pitch, yaw and yaw bearing fore-aft acceleration is less than 10%, and the surrogate model is formed. Finally, the genetic algorithm is used to optimize the heave motion response of the platform, and the extreme value of the heave response of the optimal platform scheme is further reduced by 6.98% compared with the lowest value in the database. This paper provides an intelligent method for the main scale parameter optimization of floating wind turbine platform.
关键词
海上风电机组 /
系泊 /
参数优化 /
水动力特性 /
遗传算法
Key words
offshore wind turbines /
mooring /
parameter optimization /
hydrodynamic characteristics /
genetic algorithms
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参考文献
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基金
中央高校基本科研业务费专项资金（DUT22GF202）
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