Research on probability density modeling method of wind power fluctuation based on nonparametric kernel density estimation

Daojun Chen, Hu Guo, Jian Zuo, Ting Cui, Yangwu Shen, Lei Zhang
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引用次数: 4

Abstract

Since the research on probability density distribution model of wind power fluctuation is of great importance for wind power integration and operation, this paper proposes a novel modeling method for the wind power fluctuation probability density based on nonparametric kernel density estimation. Firstly, the fluctuation components of wind power are extracted by wavelet decomposition to build a model involved bandwidth optimization, which is based on nonparametric kernel density estimation. Then a bandwidth optimization model is built constrained by goodness of fit test. Finally, constrained ordinal optimization is adopted to solve the model. Simulation results show that the model constructed by nonparametric kernel density estimation is determined by sample data without a prior probability density distribution, therefore this modelling method features with higher accuracy and more general applicability. In addition, an improved strategy proposed in this paper for nonparametric kernel density estimation also greatly improves the modeling accuracy and computational efficiency.
基于非参数核密度估计的风电波动概率密度建模方法研究
鉴于风电功率波动概率密度分布模型的研究对风电并网和运行具有重要意义,本文提出了一种基于非参数核密度估计的风电功率波动概率密度建模方法。首先,通过小波分解提取风电的波动分量,建立基于非参数核密度估计的带宽优化模型;然后在拟合优度检验约束下建立带宽优化模型。最后,采用约束有序优化方法对模型进行求解。仿真结果表明,非参数核密度估计所构建的模型是由样本数据决定的,没有先验概率密度分布,因此该建模方法具有更高的精度和更普遍的适用性。此外,本文提出的一种改进的非参数核密度估计策略也大大提高了建模精度和计算效率。
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