Statistical Analysis of Wind Resource Assessment for Different Locations in South-Western Thailand

Q3 Chemical Engineering
Attapon Chumapan, Paramin Neranon, Juntakan Taweekun
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引用次数: 0

Abstract

Weibull parameters have been widely used to evaluate wind energy potential. In this work presents wind resource assessment by statistical analysis with a Weibull distribution model for Krabi, Phuket, and Ranong weather stations in south-western Thailand. Ten-minute intervals include wind speed and wind direction of 10m from four-year records obtained by the Thai meteorological department. Four numerical methods, namely empirical method, graphical method, energy pattern factor method and maximum likelihood method are examined to estimate the Weibull parameters. The Weibull distribution obtained from each method is compared with the observed wind speed distribution by the performance tests using root mean square error, mean percentage error, and chi-square error to select a suitable method for the station area. The results revealed that the maximum likelihood method was the most accurate for Krabi and Phuket stations, and the energy pattern factor method was the most accurate for Ranong station. At a hub height of 80m, the highest mean wind speed and mean wind power density found in Krabi station were 3.25 m/s and 44.84 W/m2. The most probable wind speed value in three stations had a range from 1.80 to 2.50 m/s. The maximum wind speed carrying maximum energy found in Krabi station was 5.53 m/s. The operating probability of a wind turbine in Krabi station was 49.61%, followed by Phuket station was 46.80%, and Ranong station was 37.84%, respectively. In conclusion, all three stations had wind power potential classified as wind class 1 and can be sorted as follows: Krabi, Phuket, and Ranong stations.
泰国西南部不同地点风资源评估的统计分析
Weibull 参数已被广泛用于评估风能潜力。本研究利用 Weibull 分布模型对泰国西南部的甲米、普吉和拉廊气象站进行统计分析,从而对风能资源进行评估。10 分钟的时间间隔包括泰国气象部门四年记录中 10 米的风速和风向。研究了四种数值方法,即经验法、图形法、能量模式因子法和最大似然法,以估算 Weibull 参数。通过使用均方根误差、平均百分比误差和奇偶校验误差进行性能测试,将每种方法得到的 Weibull 分布与观测到的风速分布进行比较,以选择适合该测站区域的方法。结果表明,最大似然法对于甲米站和普吉站最为准确,而能量模式因子法对于拉廊站最为准确。在轮毂高度为 80 米时,甲米站的最高平均风速和平均风功率密度分别为 3.25 米/秒和 44.84 瓦/平方米。三个站点的最可能风速值范围在 1.80 至 2.50 米/秒之间。甲米站的最大风速为 5.53 m/s。甲米站风力涡轮机的运行概率为 49.61%,普吉站为 46.80%,拉廊站为 37.84%。总之,所有三个风力发电站的风力发电潜力都被划分为 1 级,并可分类如下:甲米风力发电站、普吉岛风力发电站、拉农风力发电站:甲米站、普吉站和拉农站。
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来源期刊
Journal of Advanced Research in Fluid Mechanics and Thermal Sciences
Journal of Advanced Research in Fluid Mechanics and Thermal Sciences Chemical Engineering-Fluid Flow and Transfer Processes
CiteScore
2.40
自引率
0.00%
发文量
176
期刊介绍: This journal welcomes high-quality original contributions on experimental, computational, and physical aspects of fluid mechanics and thermal sciences relevant to engineering or the environment, multiphase and microscale flows, microscale electronic and mechanical systems; medical and biological systems; and thermal and flow control in both the internal and external environment.
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