斐济苏瓦风能资源评估及30千瓦风力发电机设计

Krishneel A. Singh, S. S. Kutty, M. G. M. Khan, M. R. Ahmed
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引用次数: 0

摘要

斐济需要投资于可再生能源,以满足其能源需求,减少该国对进口化石燃料的依赖。投资风能项目,需要对风能资源进行详细的评估。在这项工作中,在苏瓦的一个地点测量了地面以上34米和20米的风速,为期三年,并估计了日、月、年和季节的平均值。还估计了两个高度的平均湍流强度。利用11种频域方法和贝叶斯技术对威布尔参数、平均风速和风力密度进行了估计。采用五次拟合优度检验和误差测量对这12种方法的性能进行了比较。结果表明,Lysen (EML)的平均风速为5.04 m/s,平均风功率密度(WPD)为147.79 W/m2。利用基于多目标遗传算法的Harp_Opt软件对容量为30kw的水平轴风力发电机组进行了设计和优化。采用自主设计的多目标遗传算法,利用7阶Bezier曲线进行数学参数化设计,并结合XFOIL软件进行翼型设计。利用AirfoilPrep插值升力和阻力系数,根据Harp_Opt GUI的需要,获得所需格式的数据。利用实测数据统计分析得到的威布尔参数对风力机的性能特性进行优化。输出功率曲线显示切割速度约为2m /s,额定风速为10m /s。经过第50次迭代,AEP从47.3 MWhr/年左右优化到48.3 MWhr/年。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Wind Energy Resource Assessment for Suva, Fiji and Design of a 30 kW Wind Turbine
Fiji needs to invest in renewable energy sources to meet its energy needs to reduce the country’s dependence on imported fossil fuels. For investing in wind energy projects, a detailed assessment of wind energy resource is required. In this work, wind speeds were measured at 34 m and 20 m above ground level at a site in Suva for three years and the daily, monthly, yearly and seasonal averages were estimated. Average turbulence intensities at the two heights were also estimated. The Weibull parameters, average wind speed and the wind power density were estimated by using eleven frequentist methods and a Bayesian technique. These twelve methods were compared against each other for their performance using five goodness of fit test and error measures. The best method was found to be the empirical method of Lysen (EML) which gave a mean wind speed of 5.04 m/s and a wind power density (WPD) of 147.79 W/m2. A horizontal axis wind turbine of 30 kW capacity was designed and optimized using Harp_Opt software which works on a multi-objective genetic algorithm. The blade sections (airfoils) were designed using an in-house multi-objective genetic algorithm code by mathematically parametrized 7th order Bezier curve coupled with XFOIL software. The lift and drag coefficients were interpolated using AirfoilPrep to get the data in the required format as needed by the Harp_Opt GUI. The Weibull parameters from the statistical analysis of the measured data were used to optimize the performance characteristics of the wind turbine. The output power curve shows a cut-in speed of about 2 m/s and a rated wind speed of 10 m/s. The AEP was optimized from around 47.3 MWhr/year to 48.3 MWhr/year after 50th iteration of Harp_Opt.
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