基于二元指数多项式的风速风向联合概率分布
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(三峡大学电气与新能源学院,湖北 宜昌 443002)

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通讯作者:

程 杉(1981—),男,博士,教授,博士生导师,主要从事新能源微电网运行优化与控制、电动汽车充换电设施与可再生能源集

中图分类号:

TM614

基金项目:

国家自然科学基金(51607105)


Joint probability distribution of wind speed and direction based on binary exponential polynomial
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(College of Electrical Engineering & New Energy,China Three Gorges University, Yichang 443002, China)

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    摘要:

    风能分布具有不均匀性,改进风能资源特征评估方法以提升其准确性和全面性对于风电场建设和风能高效利用至关重要。为此,提出基于二元指数多项式的风速风向联合概率分布建模方法,利用线性最小二乘法求解该模型的二元指数多项式参数,并加入归一化常数,使其满足概率密度函数特性,结合多种拟合优度指标函数进行优化,求解二元指数多项式的最优指数,从而获得拟合性能最优的风速风向联合概率分布。采用该模型拟合多个地区风电场的实测数据并与Copula模型进行对比验证,结果分析表明:由于二元指数多项式模型具有更多拟合参数,使该模型在均方根误差、决定系数、赤池信息准则以及平均绝对百分比误差等方面的指标均优于Copula模型,证明基于二元指数多项式的拟合模型可以更准确地拟合风电场的风速风向数据。

    Abstract:

    The distribution of wind energy is uneven, and improving the assessment method of wind energy resource characteristics to enhance its accuracy and comprehensiveness is crucial for wind farm construction and efficient use of wind energy. A modeling method is proposed for the joint probability distribution of wind speed and wind direction based on a binary exponential polynomial. The parameters of the binary exponential polynomial of this model are solved by using linear least squares. A normalization constant is added to make the binary exponential polynomial satisfy the characteristics of the probability density function. It combines multiple goodness-of-fit index functions to solve the optimal index of the binary exponential polynomial, so that obtains the optimal fitting performance of the joint probability distribution of wind speed and direction. The model is used to fit the measured data of wind farms in multiple regions and compared with the Copula model for verification. The results show that due to the more fitting parameters of the binary exponential polynomial, the proposed model is superior to Copula model in the aspects of root mean square error, coefficient of determination, Akaike information criterion and average absolute percentage error. It is proved that the fitting model based on the binary exponential polynomial can more accurately fit the wind speed and direction data of the wind farm.

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引用本文

熊昊然,程 杉.基于二元指数多项式的风速风向联合概率分布[J].电力科学与技术学报,2024,39(2):207-213.
XIONG Haoran, CHENG Shan. Joint probability distribution of wind speed and direction based on binary exponential polynomial[J]. Journal of Electric Power Science and Technology,2024,39(2):207-213.

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  • 在线发布日期: 2024-05-29
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