基于混合Copula优化算法的风速预测方法研究

黄宇; 张冰哲; 庞慧珍; 徐璟; 刘磊; 王彪

doi:10.19912/j.0254-0096.tynxb.2021-0431

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太阳能学报 ›› 2022, Vol. 43 ›› Issue (10) : 192-201. DOI: 10.19912/j.0254-0096.tynxb.2021-0431

基于混合Copula优化算法的风速预测方法研究

黄宇¹, 张冰哲¹, 庞慧珍¹, 徐璟¹, 刘磊², 王彪¹

作者信息 +

RESEARCH ON WIND SPEED FORECASTING METHOD BASED ON HYBRID COPULA OPTIMIZATION ALGORITHM

Huang Yu¹, Zhang Bingzhe¹, Pang Huizhen¹, Xu Jing¹, Liu Lei², Wang Biao¹

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文章历史 +

摘要

针对风电场中各风电机组风速之间存在的复杂时空相关性问题,提出一种基于混合Copula优化算法的风电场风速预测方法。该方法首先分析单一Copula函数拟合优度检验,选取合适Copula函数进行组合;其次,构建混合Copula函数模型对风电场内多风电机组风速相关性进行分析;最后应用最大期望（EM）算法求解模型相关系数并完成风速预测。结合优化算法,改进Copula函数能很好地解决风速相关性问题,为获取准确风速预测值奠定基础。以中国某地区风电场风电机组实测风速数据为例对所提方法进行验证,实验结果表明该模型可在准确分析风速相关性的基础上提高风速预测准确性。

Abstract

Complex temporal and spatial dependencies exist among wind speeds of various wind turbines in wind farms, leading to difficulties in improving the accuracy of wind-speed prediction. Analyzing the spatio-temporal dependency of wind speed, understanding the mutual influence among wind turbines remain problems to be solved. Accordingly, this paper first selects the appropriate Copula function for combination by analyzing the goodness of fit of single-Copula function; Then, by constructing the hybrid Copula function model to analyze the correlation of the wind speeds of multiple wind turbines in the wind farm; Finally, apply the expectation maximum （EM） algorithm to solve the correlation coefficient of the model and complete the wind-speed prediction. Combining optimization algorithms to improve the Copula function overcomes the difficulty in finding the spatio-temporal dependency of wind speed and lay a foundation for obtaining accurate wind-speed forecasts. The validity of the method is verified by using the measured wind-speed data of wind turbines in a certain area of China. Experimental results show that the model improves the accuracy of wind-speed prediction based on the accurate analysis of the spatio-temporal dependency of wind speed.

导出引用

黄宇, 张冰哲, 庞慧珍, 徐璟, 刘磊, 王彪. 基于混合Copula优化算法的风速预测方法研究[J]. 太阳能学报. 2022, 43(10): 192-201 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0431

Huang Yu, Zhang Bingzhe, Pang Huizhen, Xu Jing, Liu Lei, Wang Biao. RESEARCH ON WIND SPEED FORECASTING METHOD BASED ON HYBRID COPULA OPTIMIZATION ALGORITHM[J]. Acta Energiae Solaris Sinica. 2022, 43(10): 192-201 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0431

中图分类号： TM73

参考文献

[1] 胡帅, 向月, 沈晓东, 等. 计及气象因素和风速空间相关性的风电功率预测模型[J]. 电力系统自动化, 2021, 45(7): 28-36.
HU S, XIANG Y, SHEN X D, et al.Wind power prediction model considering Meteorological factor and spatial correlation of wind speed[J]. Automation of electric power systems, 2021, 45(7): 28-36.
[2] 赵征, 汪向硕. 基于CEEMD和改进时间序列模型的超短期风功率多步预测[J]. 太阳能学报, 2020, 41(7):352-358.
ZhAO Z, WANG X S. Ultra-short-term multi-step wind power prediction based on ceemd and improved time series model[J]. Acta energiae solaris sinica, 2020, 41(7): 352-358.
[3] MA Z R, CHEN H W, WANG J J, et al.Application of hybrid model based on double decomposition, error correction and deep learning in short-term wind speed prediction[J]. Energy conversion and management, 2020, 205: 112345.
[4] PENG Y J, LI Y, LEE K Y, et al.Coordinated control strategy of PMSG and cascaded H-Bridge STATCOM in dispersed wind farm for suppressing unbalanced grid voltage[J]. IEEE transactions on sustainable energy, 2021, 12(1): 349-359.
[5] 段偲默, 苗世洪, 李力行, 等. 计及预测误差动态相关性的多风电场联合出力不确定性模型[J]. 电力系统自动化, 2019, 43(22): 31-37.
DUAN S M, MIAO S H, LI L X, et al.Uncertainty model of combined output for multiple wind farms considering dynamic correlation of prediction errors[J]. Automation of electric power systems, 2019, 43(22): 31-37.
[6] 姚万业, 黄璞, 姚吉行, 等. 一种基于深度学习FRS-CLSTM风速预测模型[J].太阳能学报, 2020, 41(9): 324-330.
YAO W Y, HUANG P, YAO J X, et al.A FRS-CLSTM wind speed prediction model based on deep learning[J]. Acta energiae solaris sinica, 2020, 41(9): 324-330.
[7] 石东源, 蔡德福, 陈金富, 等. 计及输入变量相关性的半不变量法概率潮流计算[J]. 中国电机工程学报, 2012, 32(28): 104-113, 12.
SHI D Y, CAI D F, CHEN J F, et al.Probabilistic load flow calculation based on cumulant method considering correlation between input variables[J]. Proceedings of the CSEE, 2012, 32(28): 104-113, 12.
[8] SHARMA R, SHIKHOLA T, KOHLI J K.Modified fuzzy Q-learning based wind speed prediction[J]. Journal of wind engineering and industrial aerodynamics, 2020, 206: 104361.
[9] ZHANG G, LI Z X, ZHANG K, et al.Multi-objective interval prediction of wind power based on conditional copula function[J]. Journal of modern power systems and clean energy, 2019, 7(4): 802-812.
[10] 沈小军, 周冲成, 吕洪. 基于运行数据的风电机组间风速相关性统计分析[J]. 电工技术学报, 2017, 32(16): 265-274.
SHEN X J, ZHOU C C, LYU H.Statistical analysis of wind speed correlation between wind turbines based on operational data[J]. Transactions of China Electrotechnical Society, 2017, 32(16): 265-274.
[11] ZHU Q M, CHEN J F, SHI D Y, et al.Learning temporal and spatial correlations jointly: a unified framework for wind speed prediction[J]. IEEE transactions on sustainable energy, 2020, 11(1): 509-523.
[12] CHEN Y, ZHANG S, ZHANG W Y, et al.Multifactor spatio-temporal correlation model based on a combination of convolutional neural network and long short-term memory neural network for wind speed forecasting[J]. Energy conversion and management, 2019, 185: 783-799.
[13] 吴晓升, 江岳文. 基于去趋势互相关分析法的光照,温度和风速互相关性分析[J]. 中国电力, 2020, 53(6): 101-110, 127.
WU X S, JIANG Y W.A cross-correlation analysis of irradiation, temperature and wind speed based on detrended cross-correlation method[J]. Electric power, 2020, 53(6): 101-110, 127.
[14] ZENG M, LI J H, MENG Q H, et al.Temporal-spatial cross-correlation analysis of non-stationary near-surface wind speed time series[J]. Journal of Central South University, 2017, 24(3): 692-698.
[15] SUN C, ZHAO H B, XIE M, et al.Fuzzy copula model for wind speed correlation and its application in wind curtailment evaluation[J]. Renewable energy, 2016, 93:68-76.
[16] WANG Y K, MA H Q, Wang D, et al.A new method for wind speed forecasting based on Copula theory[J]. Environmental research, 2018, 160: 365-371.
[17] 卢锦玲, 於慧敏. 基于混合Copula的风光功率相关结构分析[J]. 太阳能学报, 2017, 38(11): 3188-3194.
LU J L, YU H M.Dependence structure analysis of wind and PV power based on hybrid Copula[J]. Acta energiae solaris sinica, 2017, 38(11): 3188-3194.
[18] PHILIPPE W P J, ZHANG S, EFTEKHARNEIAD S, et al. Mixed Copula-Based uncertainty modeling of hourly wind farm production for power system operational planning studies[J]. IEEE access, 2020, 8:138569-138583.
[19] NELSEN R B.An introduction to Copulas[M]. New York: Springer, 1999: 8-12.
[20] DEMPSTER A P, LAIRD N M, RUBIN D B.Maximum likelihood from incomplete data via the EM algorithm[J]. Journal of the Royal Statistical Society, 1977, 39: 11-38.
[21] 郁琛, 薛禹胜, 文福拴, 等. 风电功率预测误差的风险评估[J]. 电力系统自动化, 2015, 39(7): 52-58.
YU C, XUE Y S, WEN F S, et al.Risk assessment of wind power prediction[J]. Automation of electric power systems, 2015, 39(7): 52-58.
[22] 孙斌, 姚海涛, 刘婷. 基于高斯过程回归的短期风速预测[J]. 中国电机工程学报, 2012, 32(29): 104-109.
SUN B, YAO H T, LIU T.Short-term Wind speed forecasting based on Gaussian process regression model[J]. Proceedings of the CSEE, 2012, 32(29): 104-109.