基于精细化叠加尾流模型的海上风电场微观选址  

MICRO SITE SELECTION OF OFFSHORE WIND FARMS BASED ON REFINED SUPERIMPOSED WAKE MODEL

作　　者：黄玲玲[1] 陈昊 刘阳[1] Huang Lingling;Chen Hao;Liu Yang(Engineering Research Center of Offshore Wind Technology Ministry of Education(Shanghai University of Electric Power),Shanghai 200090,China;College of Electrical Engineering,Shanghai University of Electric Power,Shanghai 200090,China)

机构地区：[1]海上风电技术教育部工程研究中心(上海电力大学),上海200090 [2]上海电力大学电气工程学院,上海200090

出　　处：《太阳能学报》2025年第4期477-484,共8页Acta Energiae Solaris Sinica

基　　金：国家自然科学基金(52177097);上海市教育委员会科研创新计划(2021-01-07-00-07-E00122)。

摘　　要：针对传统的解析尾流叠加模型难以准确反映多台风电机组尾流影响下的风速损耗,而高精度CFD仿真计算时间过长,不适用于风电场机组微观选址优化的问题,基于质量守恒和动量守恒定律推导一种改进尾流叠加模型,并通过与FAST.Farm仿真结果的对比,论证所提改进叠加模型的精确性和快速性。构建一个以全寿命周期成本为目标函数的微观选址模型,并通过自适应被囊群算法求解该模型。通过海上风电场风电机组选址算例结果论证所提算法的有效性和优越性。The conventional analytical model of the wake superposition can not calculate accurately the wind speed losses between the wind turbines while the high-precision CFD simulation takes a long computation time.They are both not quite suitable for the micro site selection for the wind turbines.In this paper,an improved wake superposition model based on the laws of conservation of mass and momentum is proposed and its superiorities of the computing precision and efficiency are verified by comparing with the simulation results of FAST.Farm.Moreover,a micro-site selection optimization model aiming at minimizing the life-cycle cost is also presented and the solution is obtained by the adaptive tunicate swarm algorithm.The effectiveness and superiority of the proposed algorithm is demonstrated by a case study of an offshore wind farm.

关 键 词：海上风电场 尾流 微观选址 FAST.Farm仿真 被囊群算法 

分 类 号：TM614[电气工程—电力系统及自动化]