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Optimized Placement of Wind Turbines in Large-Scale Offshore Wind Farm Using Particle Swarm Optimization Algorithm

机译:基于粒子群算法的大型海上风电场风轮机优化配置

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With the increasing size of wind farms, the impact of the wake effect on wind farm energy yields become more and more evident. The arrangement of locations of the wind turbines (WTs) will influence the capital investment and contribute to the wake losses, which incur the reduction of energy production. As a consequence, the optimized placement of the WTs may be done by considering the wake effect as well as the components cost within the wind farm. In this paper, a mathematical model which includes the variation of both wind direction and wake deficit is proposed. The problem is formulated by using levelized production cost (LPC) as the objective function. The optimization procedure is performed by a particle swarm optimization (PSO) algorithm with the purpose of maximizing the energy yields while minimizing the total investment. The simulation results indicate that the proposed method is effective to find the optimized layout, which minimizes the LPC. The optimization procedure is applicable for optimized placement of WTs within wind farms and extendible for different wind conditions and capacity of wind farms.
机译:随着风电场规模的扩大,尾流效应对风电场能源产量的影响越来越明显。风力涡轮机(WT)的位置布置将影响资本投资并导致尾流损失,从而导致能源生产的减少。结果,可以通过考虑尾流效应以及风电场内的组件成本来完成WT的优化布置。本文提出了一个包括风向和尾流赤字变化的数学模型。该问题是通过使用平均生产成本(LPC)作为目标函数来表述的。优化过程由粒子群优化(PSO)算法执行,目的是在最大程度地减少总投资的同时,最大程度地提高能源产量。仿真结果表明,该方法有效地找到了优化的布局,从而使LPC最小。优化程序适用于风电场内WT的优化放置,并可扩展到不同的风况和风电场容量。

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