摘要
针对尾流效应对风电场输出功率造成的损失,文章提出了一种基于改进Jensen模型的优化方法。基于激光雷达实验数据验证了改进Jensen模型的有效性,并建立了多机组尾流叠加模型。对考虑尾流效应的风电场输出功率优化可行性进行分析,建立了风电场输出功率模型。针对标准粒子群算法过早收敛、易局部最优的缺陷进行了改进,在其迭代方程中加入二阶振荡环节,增加了粒子的多样性,提高了算法的全局搜索能力,同时保证了算法的运行速度;引入模拟退火操作,增强了算法的局部搜索能力。建立了风电场输出功率最大化优化模型,以轴向诱导因子为优化参数,利用改进粒子群算法对山西省某风电场模型进行了仿真分析。结果表明:当入流风速分别为8 m/s和12 m/s时,经改进粒子群算法优化之后,风电场输出功率分别提高了6.26%和4.59%;改进粒子群算法改善了标准粒子群算法存在的过早收敛、易局部最优的缺陷。
An optimization method is proposed to address the negative impact of wake effects on the loss of wind farm output power.The effectiveness of the improved Jensen model is verified based on the experimental data validation of the wind lidar.On this basis,a multi unit wake superposition model is established.An analysis was conducted on the feasibility of optimizing the output power of wind farms considering wake effects,and a wind farm output power model was established.The standard particle swarm optimization algorithm is improved for its premature convergence and easy local optimization.The second order oscillation link is added to its iterative equation,which increases the diversity of particles,improves the global search ability of the algorithm,and ensures the running speed of the algorithm;Introducing simulated annealing operation enhances the local search ability of the algorithm.A wind farm output power maximization optimization model was established,with axial induction factor as the optimization parameter.An improved particle swarm optimization algorithm was used to simulate and analyze a wind farm model in Shanxi Province on the MATLAB platform.The results showed that when the inflow wind speeds were 8 m/s and 12 m/s respectively,the output power of the wind farm could be increased by 6.26%and 4.59%after optimization by the improved particle swarm algorithm,at the same time,the improved particle swarm optimization algorithm improves the defects of premature convergence and easy local optimization existing in the standard particle swarm optimization algorithm.
作者
刘玉山
胡阔海
王灵梅
郭东杰
申戬林
Liu Yushan;Hu Kuohai;Wang Lingmei;Guo Dongjie;Shen Jjianlin(Shanxi University,Taiyuan 030000,China;SPIC Shanxi New Energy Co.,Ltd.,Taiyuan 030006,China)
出处
《可再生能源》
CAS
CSCD
北大核心
2023年第10期1336-1342,共7页
Renewable Energy Resources
基金
山西省重点研发计划项目(202202010101001)
山西省自然科学基金项目(202103021224023)。
关键词
风电场
尾流效应
改进粒子群算法
输出功率优化
wind farms
wake effect
improved particle swarm optimization algorithm
output poweroptimize
