南海海岛风资源评估及五十年一遇最大风速的风机选型
发布时间：2019-03-20 17:36
【摘要】：南海海岛地处深海,远离大陆,岛上用电长期依赖柴油发电,海岛军民用电及日常用水保障困难。南海海域辽阔,西沙、南沙、东沙诸岛蕴含有丰富的风能资源。随着风力发电技术日益成熟和小型风机的快速发展,在南海海岛应用风力发电技术已具有现实可行性。本文根据东沙岛、永兴岛、珊瑚岛和太平岛地面气象观测数据,对南海海岛进行了风能资源评估和风力发电机组选型的研究。论文首先收集、整理南海东沙岛、永兴岛、珊瑚岛和太平岛4个地面气象观测站1996年至2012年间的气象数据；然后运用卡方(χ2)检验和均方根(RMSE)检验对威布尔分布(Weibull)和瑞利分布(Reyleigh)进行检验,发现Weibull分布能更准确地刻画南海海岛实际数据的统计特性；利用两参数Weibull分布评估了各站点的风速和风能密度等风能资源情况；最后,根据1996年至2012年间气象数据,利用TMA 10kW、Jacobs 10kW、BWC Excel-S10kW和富兰德30kW(FL-30)风电机组的风速功率输出特性曲线,计算各站点的发电量,并基于容量系数法估算了容量系数值。评估发现FL-30的CF值最大,发电量最多,可选择FL-30为适用的典型风机；还结合Weibull分布和富兰德30kW风机的风速功率输出特性曲线估算出的数据,分析了所选风机在东沙岛、永兴岛等4个气象站点风电出力的季节性规律。分析1996年至2012年间的风速数据,提取了每日最大风速,分析了南海四岛的最大风情况,发现这4个站点的大风频次各不相同。利用PP图(Probability Plot)、QQ图(Quantile-Quantile Plot)重现期水平函数图和密度函数图诊断和检验基于广义Pareto分布的阈值模型的合理性,发现利用阈值模型能准确地拟合所调研岛屿最大风速特性；然后利用剩余函数图选取了阈值u,再根据广义P areto分布模型、利用极大似然估计法计算各站点五十年一遇最大风速。数值仿真结果表明永兴岛、珊瑚岛五十年一遇极大风值速分别为42.25m/s和的42.04m/s,适合选用Ⅱ型风机；东沙岛五十年一遇极大风值速35.20m/s,适合选用Ⅲ型风机；太平岛五十年一遇极大风45.38m/s,推荐选用Ⅰ型风电机组。
[Abstract]:The South China Sea Island is located in the deep sea, far away from the mainland. The island relies on diesel for a long time to generate electricity, and it is difficult for the island's military and people to use electricity and daily water. The South China Sea is vast, Xisha, Nansha and Dongsha islands are rich in wind energy resources. With the increasing maturity of wind power generation technology and the rapid development of small wind turbines, it is feasible to apply wind power generation technology to the South China Sea islands. Based on the meteorological observation data of Dongsha Island, Yongxing Island, Coral Island and Taiping Island, the wind energy resource evaluation and wind turbine selection of the South China Sea island are studied in this paper. Firstly, the meteorological data of four surface meteorological observatories, Dongsha Island, Yongxing Island, Coral Island and Taiping Island, in the South China Sea from 1996 to 2012 are collected and collated. Then the Chi-square (蠂 2) test and root mean square (RMSE) test were used to test the Weibull distribution (Weibull) and Rayleigh distribution (Reyleigh). It was found that the Weibull distribution could more accurately describe the statistical characteristics of the actual data of the South China Sea island. The wind energy resources such as wind speed and wind energy density are evaluated by using two-parameter Weibull distribution. Finally, based on the meteorological data from 1996 to 2012, the wind power output curves of TMA 10kW, Jacobs 10kW, BWC Excel-S10kW and Fuland 30kW (FL-30) wind turbines are used to calculate the output of each station. Based on the capacity coefficient method, the value of the capacity coefficient is estimated. It is found that FL-30 has the largest CF value and the most power generation, and FL-30 can be selected as the applicable typical fan. The seasonal rule of wind power output in Dongsha Island and Yongxing Island of the selected wind turbine in Dongsha Island and Yongxing Island is analyzed based on the data estimated from the Weibull distribution and the wind speed power output curve of the Fuland 30kW fan. The wind speed data from 1996 to 2012 are analyzed, the daily maximum wind speed is extracted, and the maximum wind speed of the four islands in the South China Sea is analyzed. It is found that the gale frequency of these four stations is different. The PP graph (Probability Plot), QQ graph (Quantile-Quantile Plot) level function graph and density function graph are used to diagnose and verify the rationality of the threshold model based on the generalized Pareto distribution. It is found that the threshold model can accurately fit the maximum wind velocity characteristics of the investigated islands. Then the threshold u is selected by the residual function graph and the maximum wind speed is calculated by maximum likelihood estimation method according to the generalized P-areto distribution model. The results of numerical simulation show that the maximum wind velocity of Yongxing Island and Coral Island is 42.04m / s of 42.25m/s and 42.04m / s, respectively, which is suitable for the selection of type 鈪，
本文编号：2444448
[Abstract]:The South China Sea Island is located in the deep sea, far away from the mainland. The island relies on diesel for a long time to generate electricity, and it is difficult for the island's military and people to use electricity and daily water. The South China Sea is vast, Xisha, Nansha and Dongsha islands are rich in wind energy resources. With the increasing maturity of wind power generation technology and the rapid development of small wind turbines, it is feasible to apply wind power generation technology to the South China Sea islands. Based on the meteorological observation data of Dongsha Island, Yongxing Island, Coral Island and Taiping Island, the wind energy resource evaluation and wind turbine selection of the South China Sea island are studied in this paper. Firstly, the meteorological data of four surface meteorological observatories, Dongsha Island, Yongxing Island, Coral Island and Taiping Island, in the South China Sea from 1996 to 2012 are collected and collated. Then the Chi-square (蠂 2) test and root mean square (RMSE) test were used to test the Weibull distribution (Weibull) and Rayleigh distribution (Reyleigh). It was found that the Weibull distribution could more accurately describe the statistical characteristics of the actual data of the South China Sea island. The wind energy resources such as wind speed and wind energy density are evaluated by using two-parameter Weibull distribution. Finally, based on the meteorological data from 1996 to 2012, the wind power output curves of TMA 10kW, Jacobs 10kW, BWC Excel-S10kW and Fuland 30kW (FL-30) wind turbines are used to calculate the output of each station. Based on the capacity coefficient method, the value of the capacity coefficient is estimated. It is found that FL-30 has the largest CF value and the most power generation, and FL-30 can be selected as the applicable typical fan. The seasonal rule of wind power output in Dongsha Island and Yongxing Island of the selected wind turbine in Dongsha Island and Yongxing Island is analyzed based on the data estimated from the Weibull distribution and the wind speed power output curve of the Fuland 30kW fan. The wind speed data from 1996 to 2012 are analyzed, the daily maximum wind speed is extracted, and the maximum wind speed of the four islands in the South China Sea is analyzed. It is found that the gale frequency of these four stations is different. The PP graph (Probability Plot), QQ graph (Quantile-Quantile Plot) level function graph and density function graph are used to diagnose and verify the rationality of the threshold model based on the generalized Pareto distribution. It is found that the threshold model can accurately fit the maximum wind velocity characteristics of the investigated islands. Then the threshold u is selected by the residual function graph and the maximum wind speed is calculated by maximum likelihood estimation method according to the generalized P-areto distribution model. The results of numerical simulation show that the maximum wind velocity of Yongxing Island and Coral Island is 42.04m / s of 42.25m/s and 42.04m / s, respectively, which is suitable for the selection of type 鈪，
本文编号：2444448
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