高原气象

高原气象 >

2018 , Vol. 37 >Issue 4: 1061 - 1073

DOI: https://doi.org/10.7522/j.issn.1000-0534.2017.00098

论文

集成CFD与Kalman滤波的微尺度风电场风功率预报方法

  • 刘丽珺 ,
  • 梁友嘉
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  • 武汉理工大学航运学院, 湖北 武汉 430063;内河航运技术湖北省重点实验室, 湖北 武汉 430063;国家水运安全工程技术研究中心, 湖北 武汉 430063;武汉理工大学资源与环境工程学院, 湖北 武汉 430070

收稿日期: 2017-07-12

  网络出版日期: 2018-08-28

基金资助

国家自然科学基金项目（41601184）

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Wind Power Prediction Method for Micro-scale Wind Farm Based on CFD and Kalman Filtering Integrated Correction

  • LIU Lijun ,
  • LIANG Youjia
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  • School of Navigation, Wuhan University of Technology, Wuhan 430063, Hubei, China;Hubei Key Laboratory of Inland Shipping Technology, Wuhan 430063, Hubei, China;National Engineering Research Center for Water Transport Safety, Wuhan 430063, Hubei, China;School of Resources and Environmental Engineering, Wuhan 430070, Hubei, China

Received date: 2017-07-12

  Online published: 2018-08-28

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摘要

基于Kalman滤波的风功率预测方法难以捕获风电场的风力空间分布特征，集成计算流体力学CFD模型和Kalman滤波能将风资源分布纳入风力发电预报框架，对提高微尺度风能利用率有重要意义。以黄土高原沟壑区的中国华电集团公司甘肃省环县南湫风电场为研究区，首先利用CFD模拟风电场风速分布特征和划分风区，再利用Kalman滤波订正BJ-RUC模式预报的各风区长时间序列风速变化，并对比不同风区的订正效果，最终基于发电能力评估风电场的风功率预报效果与效益。结果表明：（1）风电场内部的风机均不同程度地受到复杂地形的影响，围绕"马蹄形"山梁和山谷形成明显的"阶梯式"风区，风速差最高达2.78 m·s-1；（2）CFD与Kalman滤波的集成订正方法使风速预报准确率由BJ-RUC的20%~30%提高到90%以上，并使风功率预报准确率达到80%以上，显著提高了微尺度沟壑区风速-风功率预报精度；（3）风电场容量因子CP平均在12.4%~16.8%之间，弃风率η为5.5%~17.5%，表明该电场的风功率预报精度明显受其发电效益制约；（4）还讨论了风电机组监控数据采集控制系统SCADA的数据质量、CFD计算效率和能源部门决策等不确定性因素对风速分区及风功率预报的影响。

关键词： 风能; BJ-RUC模式; CFD; Kalman滤波; 弃风率

本文引用格式

刘丽珺 , 梁友嘉 . 集成CFD与Kalman滤波的微尺度风电场风功率预报方法[J]. 高原气象, 2018 , 37(4) : 1061 -1073 . DOI: 10.7522/j.issn.1000-0534.2017.00098

Abstract

As a statistical method, Kalman filter is often employed for power forecasting, but the method is not able to capture tails of wind power distributions.In this paper, a hybrid method, based on the combination of Kalman filter and a physical method using computational fluid dynamics (CFD), considered the factor of wind resource distribution and will be useful to improve the availability of micro-scale wind energy.It is of great significance to improve the utilization of wind energy at the micro-scale.Taking Nanqiu wind farm as the study area, which locates in the Loess Plateau of Gansu Province.SCADA was provided by China Huadian Corporation (CHD).Firstly, CFD method was used to simulate the wind environment and the characteristics of wind speed, and the results of CFD were then analyzed and the sub-regions of wind prediction were set up.Secondly, the predicted wind speed was corrected by Kalman filter, which was based on the simulation results by BJ-RUC model.Thirdly, after the adjustment with the Kalman filter method, the forecasting accuracy of wind speed was improved for the wind station and all the sub-regions.Finally, the effects of wind power forecasting were assessed and the correspond benefits were also evaluated, which based on the power generation capacity of the wind farm.The results showed that:(1) Wind speed of the airflow over the mountains reaches maximum.When mountain height increases, the wind speed values increases, the difference of wind speed is up to 2.78 m·s-1; (2) The result of BJ-RUC model showed that the model could resolve the variation trend of wind speed, the accuracy of wind speed has better performs with accuracy rate over 90% from the initial accuracy 20%~30% by integrating Kalman filter and CFD method.Meanwhile, the accuracy of wind power forecast in micro-scale Loess Plateau is up to 80%, the wind speed and wind power forecasting accuracy has been significantly improved at the micro-scale complex terrain; (3) The capacity factor CP is on average about 12.4%~16.8%, curtailment rates η is on average about 5.5%~17.5%, which affects the forecasting effect of wind power, so the power generation efficiency should be improved furtherly; (4) In addition, some uncertainty factors were also discussed in the paper, including data quality of SCADA, computational efficiency of CFD, policy decision of energy sector, and the way in which it effects characteristics and forecast of wind speed.

Key words： Wind energy; BJ-RUC model; Computational Fluid; Kalman filter; curtailment rate

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