基于密度修正的风电功率曲线线性拟合模型

doi:10.3969/j.issn.2097-0706.2025.03.008

综合智慧能源 ›› 2025, Vol. 47 ›› Issue (3): 84-91.doi: 10.3969/j.issn.2097-0706.2025.03.008

基于密度修正的风电功率曲线线性拟合模型

朱东杰¹(), 吕昆烨²^,^*(), 宋长虹², 江美慧³^,⁴, 李枝玖²

1.中国华电集团贵港发电有限公司，广西贵港 537100
2.广西电力职业技术学院电力工程学院，南宁 530001
3.广西大学电气工程学院，南宁 530004
4.内蒙古工业大学新能源学院，内蒙古鄂尔多斯 017010

收稿日期:2024-12-12 修回日期:2025-02-04 接受日期:2025-03-25 出版日期:2025-03-25
通讯作者: *吕昆烨（1995），男，助教，硕士，从事风电建模、分布式发电与微电网方面的研究，kunyelv@yeah.net。
作者简介:朱东杰（1980），男，工程师，硕士，从事新能源发电方面的研究，zhudongjie80@yeah.net。
基金资助:
国家自然科学基金项目(52107083);广西科技重大专项(AA22068071)

Linear fitting model for wind power curves based on density correction

ZHU Dongjie¹(), LYU Kunye²^,^*(), SONG Changhong², JIANG Meihui³^,⁴, LI Zhijiu²

1. China Huadian Corporation Guigang Power Company Limited， Guigang 537100， China
2. School of Electrical Engineering， Guangxi Electrical Polytechnic Institute， Nanning 530001， China
3. School of Electrical Engineering， Guangxi University， Nanning 530004， China
4. School of Renewable Energy， Inner Mongolia University of Technology， Ordos 017010， China

Received:2024-12-12 Revised:2025-02-04 Accepted:2025-03-25 Published:2025-03-25
Supported by:
National Natural Science Foundation of China(52107083);Guangxi Science and Technology Major Project(AA22068071)

摘要/Abstract

摘要：

针对传统风电功率曲线难以计及气象因素的影响而导致模型精度变低的问题，提出了一种考虑密度修正的风电功率曲线线性拟合模型LI-DASW。建立了基于气温、气压、湿度等气象因素的空气密度计算模型以及密度修正风速策略，既能反映气象因素变化对风电功率曲线的影响，又能保持模型的单输入单输出特性。以一阶矩替代原数据集合建模，减少冗余计算，提升建模效率；然后以一阶矩为插值点构建线性插值模型，有效规避了高阶多项式拟合带来的Runge现象，增强模型适应性。两个风电场的算例分析结果表明，LI-DASW模型拟合性能明显优于传统方法：相较于Bin法，模型的均方根误差（RMSE）分别降低了14.42%和10.16%，平均绝对误差（MAE）降幅达15.63%和9.48%；与多项式方法相比，RMSE降幅提升至20.33%和7.66%，MAE改进幅度分别为18.15%和8.06%；相较于线性插值方法，RMSE和MAE降低了6.19%∼7.37%；同时，建模效率较多项式模型提高了84.81%以上。

关键词: 风电, 功率曲线, 线性拟合模型, 气象因素, 密度修正风速策略, 一阶矩, 线性插值

Abstract:

To address the issue that traditional wind power curve models fail to fully consider the effect of meteorological factors， resulting in reduced model accuracy， a new linear fitting model for wind power curves named LI-DASW is proposed with the inclusion of density correction. A calculation model for air density was developed based on meteorological factors such as temperature， pressure， and humidity， as well as a density-corrected wind speed strategy. It reflected the effect of meteorological changes on the wind power curve while maintaining the model's single-input and single-output characteristics. The original dataset was replaced with the first moment to reduce redundant calculations and improve modeling efficiency. A linear interpolation model was constructed using the first moment as interpolation points， effectively avoiding the Runge's phenomenon caused by higher-order polynomial fitting and enhancing the model's adaptability. The case study analysis results of two wind farms demonstrated that the LI-DASW model significantly outperformed traditional methods. Compared to the Bin method， the model's root mean square error（RMSE） reduced by 14.42% and 10.16%， and the mean absolute error（MAE） decreased by 15.63% and 9.48%， respectively. Compared to the polynomial method， the RMSE decreased by 20.33% and 7.66%， and the MAE improved by 18.15% and 8.06%. Compared to the linear interpolation method， the reductions in RMSE and MAE remained stable between 6.19% to 7.37%. Additionally， the modeling efficiency improved by over 84.81% compared to the polynomial model.

Key words: wind power, power curve, linear fitting model, meteorological factors, density-corrected wind speed strategy, first moment, linear interpolation

中图分类号:

TK81

朱东杰, 吕昆烨, 宋长虹, 江美慧, 李枝玖. 基于密度修正的风电功率曲线线性拟合模型[J]. 综合智慧能源, 2025, 47(3): 84-91.

ZHU Dongjie, LYU Kunye, SONG Changhong, JIANG Meihui, LI Zhijiu. Linear fitting model for wind power curves based on density correction[J]. Integrated Intelligent Energy, 2025, 47(3): 84-91.

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参考文献 27

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参数	单位	数值
α		1.000 62
β	Pa^-1	3.14×10^-8
γ	K^-2	5.60×10^-7

参数	单位	数值
A	K^-2	1.237 884 7×10^-5
B	K^-1	-1.912 131 6×10^-2
C		33.937 110 47
D	K	-6.343 164 5×10³

参数	单位	数值
a₀	K/Pa	1.581 23×10^-6
a₁	Pa^-1	-2.933 1×10^-8
a₂	K/Pa	1.104 3×10^-10
b₀	K/Pa	5.707 0×10^-6
b₁	Pa^-1	-2.051×10^-8
c₀	K/Pa	1.989 8×10^-4
c₁	Pa^-1	-2.376×10^-6
d	K²/Pa²	1.830×10^-11
e	K²/Pa²	-0.765×10^-8

模型	甲电场		乙电场
模型	RMSE	MAE	RMSE	MAE
Bin	2.757 7	1.603 8	4.613 9	3.166 4
PR	2.962 3	1.653 1	4.487 1	3.117 1
LI	2.515 7	1.448 8	4.456 8	3.094 1
LI-DASW	2.360 0	1.353 1	4.144 9	2.866 0

模型		Bin	PR	LI	LI-DASW
时间复杂度		O(n)	O(nk²)	O(n)	O(n)+O(1)
建模耗时/s	甲电场	0.004	0.098	0.010	0.011
建模耗时/s	乙电场	0.005	0.079	0.010	0.012

基于密度修正的风电功率曲线线性拟合模型

Linear fitting model for wind power curves based on density correction

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