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ISSN 0254-0096 CN 11-2082/K

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

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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   

  1. 1. 华北电力大学自动化系,保定 071003;
    2. 华北电力科学研究院有限责任公司,北京 100045
  • 收稿日期:2021-04-20 出版日期:2022-10-28 发布日期:2023-04-28
  • 通讯作者: 黄宇(1982——),男,博士、副教授,主要从事综合能源系统优化与控制方面的研究。2192216010@ncepu.edu.cn
  • 基金资助:
    中央高校基本科研业务费专项资金(2021MS089)

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

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

  1. 1. Department of Automation, North China Electric Power University, Baoding 071003, China;
    2. North China Electric Power Research Institute Co., Ltd., Beijing 100045, China
  • Received:2021-04-20 Online:2022-10-28 Published:2023-04-28

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。

关键词: 风速, 相关性, Copula函数, EM算法

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.

Key words: wind speed, correlation, Copula function, EM algorithm

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