潮流能涡轮机阵列优化离散量子粒子群算法

Quantum discrete particle swarm algorithm of tidal turbine array optimization

为解决多参数、多约束条件的潮流能涡轮机阵列优化问题,提出了一种改进的离散量子粒子群(quantum discrete particle swarm,QDPS)算法。该算法将计算区域离散化,每个粒子代表一种涡轮机阵列布局,并以发电量为目标函数,利用更新公式进行迭代优化。基于舟山普陀山岛—葫芦岛水道涨急和落急时刻的流场数据进行算法验证,分析了涡轮机阵列优化效果。结果表明:离散量子粒子群算法能够实现自主智能优化,优化速度快,与传统交错布局相比,涨急时刻涡轮机阵列总发电量提高了28.9%,落急时刻涡轮机阵列总发电量提高了41.8%,阵列优化布局结果与潮流能功率密度分布是一致的。离散量子粒子群算法可为潮流能发电场涡轮机阵列布局优化研究提供科学工具。

To solve the problem of multi-parametermultiparameter and multi-constraint tidal turbine array optimization,an improved quantum discrete particle swarm(QDPS)algorithm is proposed.In the QDPS algorithm,the computational domain is discretized and each particle represents a turbine array layout.The generated energy is taken asconsidered the objective function and updated through iterative optimization.The QDPS algorithm is verified using flood and ebb spring tide data from the Pu-Hu waterway in the Zhoushan Islands of China,and the optimized effect is analyzed.The results show that autonomous intelligent optimization can be achieved by using the QDPS algorithm and the optimal velocity is rapid.Compared with the traditional crossing layout,28.9%of total power generation is improved in the flood tide and 41.8%in the ebb tide.The optimized tidal turbine array layout is consistent with the power density distribution of the power density of tidal current energy.In summary,as a scientific tool,the QDPS algorithm can be used to study on tidal turbine array optimization.