集成CFD与Kalman滤波的微尺度风电场风功率预报方法

doi:10.7522/j.issn.1000-0534.2017.00098

高原气象

2018, Vol. 37

Issue (4): 1061-1073 DOI: 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^1,2,3, 梁友嘉⁴

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

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

LIU Lijun^1,2,3, LIANG Youjia⁴

1. School of Navigation, Wuhan University of Technology, Wuhan 430063, Hubei, China;
2. Hubei Key Laboratory of Inland Shipping Technology, Wuhan 430063, Hubei, China;
3. National Engineering Research Center for Water Transport Safety, Wuhan 430063, Hubei, China;
4. School of Resources and Environmental Engineering, Wuhan 430070, Hubei, China

全文: PDF

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C_P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

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 C_P 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 Dynamics Kalman filter curtailment rate

收稿日期: 2017-07-12 出版日期: 2018-08-22

P339

基金资助: 国家自然科学基金项目（41601184）

通讯作者: 梁友嘉(1985-),男,甘肃庆阳人,讲师,主要从事生态系统评价和环境建模研究.E-mail:yjliang@whut.edu.cn E-mail: yjliang@whut.edu.cn

作者简介: 刘丽珺(1988-),女,甘肃定西人,博士研究生,主要从事CFD风场模拟研究.E-mail:chenshi2124@126.com

	服务
	把本文推荐给朋友
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	刘丽珺
	梁友嘉

引用本文:

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.

LIU Lijun, LIANG Youjia. Wind Power Prediction Method for Micro-scale Wind Farm Based on CFD and Kalman Filtering Integrated Correction. Plateau Meteorology, 2018, 37(4): 1061-1073.

链接本文:

http://www.gyqx.ac.cn/CN/10.7522/j.issn.1000-0534.2017.00098 或 http://www.gyqx.ac.cn/CN/Y2018/V37/I4/1061

Addison J F D, Hunter A, Bass J, et al, 2000. A neural network version of the measure correlate predict algorithm for estimating wind energy yield[J]. G730 Neural Computing.
Benjamin S G, Dévényi D, Weygandt S S, et al, 2004. An hourly assimilation-forecast cycle:The RUC[J]. Mon Wea Rev, 132(2):495-518. DOI:10.1175/1520-0493(2004)132<0495:AHACTR>2.0. CO; 2.
Cassola F, Burlando M, 2012. Wind speed and wind energy forecast through Kalman filtering of numerical weather prediction model output[J]. Appl Energy, 99:154-166. DOI:10.1016/j. apenergy. 2012.03.054.
Castellani F, Burlando M, Taghizadeh S, et al, 2014. Wind energy forecast in complex sites with a hybrid neural network and CFD based method[J]. Energy Procedia, 45:188-197. DOI:10.1016/j. egypro. 2014.01.021.
Counehan J, 1971. Wind tunnel determination of the roughness length as a function of the fetch and the roughness density of three-dimensional roughness elements[J]. Atmos Environ, 5(8):637-642. DOI:10.1016/0004-6981(71)90120-X.
Doucoure B, Agbossou K, Cardenas A, 2016. Time series prediction using artificial wavelet neural network and multi-resolution analysis:Application to wind speed data[J]. Renewable Energy, 92:202-211. DOI:10.1016/j. renene. 2016.02.003.
Foley A M, Leahy P G, Marvuglia A, et al, 2012. Current methods and advances in forecasting of wind power generation[J]. Renewable Energy, 37(1):1-8. DOI:10.1016/j. renene. 2011.05.033.
Iglesias G, Castellanos P, Seijas A, 2010. Measurement of productive efficiency with frontier methods:A case study for wind farms[J]. Energy Economics, 32(5):1199-1208. DOI:10.1016/j. eneco. 2010.03.004.
Johnson G L, 2006. Wind energy systems[M]. Gary L. Johnson.
Kusiak A, Li W, 2011. The prediction and diagnosis of wind turbine faults[J]. Renewable Energy, 36(1):16-23. DOI:10.1016/j. renene. 2010.05.014.
Kusiak A, Verma A. 2013. Monitoring wind farms with performance curves[J]. IEEE Trans Sustain Energy, 4(1):192-199. DOI:10.1109/TSTE. 2012.2212470.
Landberg L, 2001. Short-term prediction of local wind conditions[J]. Journal of Wind Engineering and Industrial Aerodynamics, 89(3):235-245. DOI:10.1007/BF00712528.
Launder B E, Spalding D B, 1974. The numerical computation of turbulent flows[J]. Computer Methods In Applied Mechanics And Engineering, 3(2):269-289. DOI:10.1016/0045-7825(74)90029-2.
Liu S, Pan W, Zhang H, et al, 2017. CFD simulations of wind distribution in an urban community with a full-scale geometrical model[J]. Building and Environment, 117:11-23. DOI:10.1016/j. buildenv. 2017.02.021.
Louka P, Galanis G, Siebert N, et al, 2008. Improvements in wind speed forecasts for wind power prediction purposes using Kalman filtering[J]. Journal of Wind Engineering and Industrial Aerodynamics, 96(12):2348-2362. DOI:10.1016/j. jweia. 2008.03.013.
Lundtang P E, Mortensen N G, Landberg L, et al, 1997. Wind power meteorology[R]. Risø National Laboratory.
Magnusson M, Wern L, 2001. PG1.54 Wind energy predictions using CFD and HIRLAM forecasts[C]//EWEC-CONFERENCE. 861-863.
Mariukaitisa M, Katinas V, Geceviius G, 2015. Analysis of economic, environment, social factors and efficiency of wind turbines in Lithuania[C]//Trends and challenges for wind energy harvesting:workshop, March 30-31, Coimbra, Portugal.
Schlechtingen M, Santos I F, Achiche S, 2013. Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 1:System description[J]. Applied Soft Computing, 13(1):259-270. DOI:10.1016/j. asoc. 2012.08.033.
Song M X, Chen K, Zhang X, et al, 2015. The lazy greedy algorithm for power optimization of wind turbine positioning on complex terrain[J]. Energy, 80:567-574. DOI:10.1016/j. energy. 2014.12.012.
Torres J L, Prieto E, Garcia A, et al, 2003. Effects of the model selected for the power curve on the site effectiveness and the capacity factor of a pitch regulated wind turbine[J]. Solar Energy, 74(2):93-102. DOI:10.1016/S0038-092X(03)00144-0.
Yang W, Court R, Jiang J, 2013. Wind turbine condition monitoring by the approach of SCADA data analysis[J]. Renewable Energy, 53:365-376. DOI:10.1016/j. renene. 2012.11.030.
陈正洪, 2013. 风电功率预测预报技术原理及其业务系统[M]. 北京:气象出版社. Chen Z H, 2013. The principle of wind power forecasting and forecasting technology and its operational system[M]. Beijing:China meteorological press.
程兴宏, 陶树旺, 魏磊, 等, 2012. 基于 WRF 模式和自适应偏最小二乘回归法的风能预报试验研究[J]. 高原气象, 31(5):1461-1469. Cheng X H, Tao S W, Wei L, et al, 2012. Short-term wind power forecasting experiment based on WRF Model and adapting partial least square regression method[J]. Plateau Meteor, 31(5):1461-1469.
丁煌, 陶树旺, 肖子牛, 等, 2013. 基于 WRF 和 SVM 方法的风电场功率预报技术研究[J]. 高原气象, 32(2):581-587. Ding H, Tao S W, Xiao Z N, et al, 2013. Study on wind power forecasting of wind farm based on WRF and SVM[J]. Plateau Meteor, 32 (2):581-587. DOI:10.7522/j. issn. 1000-0534.2012.00056.
国家电监会, 2013. 风电场弃风电量计算办法(试行)[S]. State Electricity Regulatory Commission, 2013. Wind power calculation method for wind farm (Trial)[S].
国家技术监督局, 国家环境保护局, 1992. GB/T 3840-1991 制定地方大气污染物排放标准的技术方法[S]. State Bureau of Technology Supervision, State Environmental Protection Administration, 1992. GB/T 3840-1991 Technical methods for making local emission standards of air pollutants[S].
国家能源局, 2014. NB/T 31046-2013 风电功率预测系统功能规范[S]. National Energy Administration, 2014. NB/T 31046-2013 Function specification of wind power forecasting system[S].
国家能源局, 2016.2016年一季度风电并网运行情况[J]. 中国能源, 38(5):33-33. National Energy Administration, 2016. Wind power grid operation in the first quarter of 2016[J]. Energy of China, 38(5):33-33.
国家能源局, 2017.2016年风电消纳情况公布[J]. 风能, 4:8. National Energy Administration. 2017. Announcement of wind power consumption in 2016[J]. Wind energy, 4:8.
国家质检总局, 2002. GB/T 18710-2002 风电场风能资源评估方法[S]. General Administration of Quality Supervision, 2002. GB/T 18710-2002 Methodology of wind energy resource assessment for wind farm[S].
李芬, 陈正洪, 蔡涛, 等, 2013. 并网光伏系统性能精细化评估方法研究[J]. 太阳能学报, 34(6):974-983. Li F, Chen Z H, Cai T, et al, 2013. Refinement assessment method of grid-connected PV system performance[J]. Acta Energiae Solaris Sinica, 34(6):974-983.
刘丽珺, 梁友嘉, 2016. BJ-RUC 模式的低层风场预报产品质量检验——以甘肃省当金山风电场为例[J]. 干旱气象, 34(4):743-751. Liu L J, Liang Y J, 2016. Quality assessment of low-level wind prediction product by BJ-RUC Model-A case study of akesai wind farm in Gansu Province[J]. J Arid Meteor, 34(4):743-751. DOI:10.11755/j. issn. 1006-7639(2016)-04-0743.
刘丽珺, 吕萍, 梁友嘉, 2013. 基于CFD技术的河谷型城市风环境模拟——以兰州市城关区为例[J]. 中国沙漠, 33(6):1840-1847. Liu L J, Lv P, Liang Y J, 2013. Simulating wind environment of valley city based CFD technology:A case study of Chengguan District of Lanzhou, China[J]. J Desert Res, 33(6):1840-1847. DOI:10.7522/j. issn. 1000-694X. 2013.00244.
柳艳香, 陶树旺, 张秀芝, 2008. 风能预报方法研究进展[J]. 气候变化研究进展, 4(4):209-214. Liu Y X, Tao S W, Zhang X Z, 2008. Review on methods of wind power forecasting[J]. Climate Change Res, 4(4):209-214.
陆如华, 徐传玉, 张玲, 等, 1997. Kalman滤波的初值计算方法及其应用[J]. 应用气象学报, 8(1):34-43. Lu R H, Xu C Y, Zhang L, et al, 1997. Initial value calculation method of Kalman filter and its application[J]. Quart J Appl Meteor, 8(1):34-43.
石岚, 徐丽娜, 郝玉珠, 2017. 多模式风速融合预报应用研究[J]. 高原气象, 36(4):1022-1028. Shi L, Xu L N, Hao Y Z, 2017. Application research on the Multi-Model fusion forecast of wind speed[J]. Plateau Meteor, 36(4):1022-1028. DOI:10.7522/j. issn. 1000-0534.2017.00021.
王澄海, 胡菊, 靳双龙, 等, 2011. 中尺度WRF模式在西北西部地区低层风场模拟中的应用和检验[J]. 干旱气象, 29(2):161-167. Wang C H, Hu J, Jin S L, et al, 2011. Application and test of lower level wind field simulation with meso-scale WRF in western region of Northwest China[J]. J Arid Meteor, 29(2):161-167.
王丹云, 吕世华, 韩博, 等, 2017. 近30 年黄土高原春季降水特征与春旱变化的关系[J]. 高原气象, 36(2):395-406. Wang D Y, Lv S H, Han B, et al, 2017. Characteristics of spring precipitation and their connections with the spring drought over the Loess Plateau during last three decades[J]. Plateau Meteor, 36(2):395-406. DOI:10.7522/j. issn. 1000-0534.2016.00121.
魏东, 尤凤春, 范水勇, 等, 2010. 北京快速更新循环预报系统(BJ-RUC)模式探空质量评估分析[J]. 气象, 36(8):72-80. Wei D, You F C, Fan S Y, et al, 2010. Analysis of radial quality evaluation of Beijing rapid renewal circulation forecasting system (BJ-RUC) Model[J]. Meteor Mon, 36(8):72-80. DOI:10.7519/j. issn. 1000-0526.2011.12.003.
张双益, 胡非, 2017. 大气边界层与风力发电的相互作用研究综述[J]. 高原气象, 36(4):1127-1137. Zhang S Y, Hu F, 2017. Review on study of atmospheric boundary layer and wind power generation interaction[J]. Plateau Meteor, 36(4):1127-1137. DOI:10.7522/j. issn. 1000-0534.2016.00095.
张新科, 陈晋北, 余晔, 等, 2017. 雷暴系统影响下的黄土高原塬区微气象特征研究[J]. 高原气象, 36(2):384-394. Zhang X K, Chen J B, Yu Y, et al, 2017. Study on the micrometeorological characteristics over the Loess Plateau under the influence of thunderstorm[J]. Plateau Meteor, 36(2):384-394. DOI:10.7522/j. issn. 1000-0534.2017.00002.
祝赢, 柳艳香, 严金蓉, 等, 2012. 基于卡尔曼滤波的模式预报风速订正方法研究[C]//全国环境风工程学术会议. Zhu Y, Liu Y X, Yan J R, et al, 2012. Study on model prediction wind speed correction method based on Calman filtering[C]//National Conference on environmental wind engineering.

[1]	王顺久. 青藏高原积雪变化及其对中国水资源系统影响研究进展[J]. 高原气象, 2017, 36(5): 1153-1164.
[2]	马耀明, 姚檀栋, 王介民. 青藏高原能量和水循环试验研究——GAME/Tibet与CAMP/Tibet研究进展[J]. 高原气象, 2006, 25(2): 344-351.
[3]	高艳红, 吕世华. 黑河上游环境要素场对降雨汇流过程的响应[J]. 高原气象, 2004, 23(2): 184-191.
[4]	范广洲, 吕世华, 程国栋. 气候变化对滦河流域水资源影响的水文模式模拟(Ⅱ):模拟结果分析[J]. 高原气象, 2001, 20(3): 302-310.
[5]	马柱国. 葛州坝、唐乃亥径流量的时变特征分析[J]. 高原气象, 1995, 14(1): 19-27.

Viewed

Full text

Abstract

Cited

Shared

Discussed