Regional Wind Power Prediction Considering Temporal and Spatial Characteristics

doi:10.7652/xjtuxb201310012

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Regional Wind Power Prediction Considering Temporal and Spatial Characteristics

更新时间：2025-07-09

- Regional Wind Power Prediction Considering Temporal and Spatial Characteristics
- Vol. 47, Issue 10, Pages: 68-74(2013)
- 作者机构：
  
  西安交通大学电力设备电气绝缘国家重点实验室,西安,710049
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201310012
  CLC： TM614
- Online First：10 October 2013，
  
  Published：2013
- 稿件说明：
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Regional Wind Power Prediction Considering Temporal and Spatial Characteristics[J]. 2013, 47(10): 68-74.
DOI：

Regional Wind Power Prediction Considering Temporal and Spatial Characteristics[J]. 2013, 47(10): 68-74. DOI： 10.7652/xjtuxb201310012.

摘要

为了有效解决风电场数据丢失时直接相加法无法进行区域风电功率预测的问题

提出了一种考虑时空分布特性的区域风电功率预测方法。为降低模型的复杂性

根据风电场及风能信息对子区域进行具体分析。在此基础上

利用相关系数法

选择风电场出力与子区域出力间相关系数绝对值大的风场为基准风电场。以所选基准风电场预测功率为输入

利用神经网络方法

直接预测各子区域功率

整个区域预测结果为各子区域预测值之和。算例结果表明:利用相关系数法选择基准风电场无需大量历史数据支撑

原理简单易于实现; 模型与风电场所采用的预测系统无关

易于工程推广应用; 模型无需考虑非基准风电场功率预测情况

成本更低、效益更高; 采用该模型后子区域预测误差比直接相加的方法降低了5%

整个区域预测误差仅为20.8%。

Abstract

A regional wind power prediction method considering temporal and spatial characteristics is proposed to effectively solve the unavailability of direct-adding-up method in the case of missing data. To reduce the model complexity

the region is divided into several sub-regions according to the information from this region. Furthermore

a correlation coefficient method is proposed to select reference wind farms. The correlation coefficient between wind-farm power output and sub-region power output is calculated separately and compared with each other. Farms with larger absolute correlation coefficient are chosen as the reference ones. A back propagation neural network is adopted to directly predict wind power output of each sub-region

and the predicted power of reference farms in this sub-region is considered as the input. The regional wind power prediction can be achieved after summing the prediction results of sub-regions. The application shows that the correlation coefficient method needs less history data and is easy to realize. The proposed model is compatible with various wind farm power prediction systems and independent on the power prediction of non-reference farms

thus the prediction is more efficient with lower cost. The root mean square error declines by 5% compared with that in direct-adding-up method

and the regional wind power prediction error reaches only 20.8%.

关键词

Keywords

references

FOCKEN U, LANGE M, MÖNNICH K, et al. Short-term prediction of the aggregated power output of wind farms: a statistical analysis of the reduction of the prediction error by spatial smoothing effects [J]. Journal of Wind Engineering and Industrial Aerodynamics, 2002, 90(3): 231-246.

HAN Yu, CHANG Liuchen. A study of the reduction of the regional aggregated wind power forecast error by spatial smoothing effects in the maritime Canada [C]∥2010 IEEE Electric Power and Energy Conference. New York, USA: IEEE Computer Society, 2010: 942-947.

NANAHARA T, ASARI M, MAEJIMA T, et al. Smoothing effects of distributed wind turbines: part 2 coherence among power output of distant wind turbines [J]. Wind Energy, 2004, 7(2): 75-85.

FOCKEN U, LANGE M, WALDL H P. Previento: a wind power prediction system with an innovative upscaling algorithm [C]∥European Wind Energy Conference. Brussels, Belgium: European Wind Energy Association, 2001: 826-829.

ISHIHARA T, YAMAGUCHI A, OGAWA T, et al. An upscaling approach for the regional wind power forecasting [C]∥European Wind Energy Conference and Exhibition. Brussels, Belgium: European Wind Energy Association, 2007: 948-951.

NIELSEN T S, MADSEN H, NIELSEN H A, et al. Prediction of regional wind power [EB/OL]. [2013-02-22]. http:∥orbit.dtu.dk/fedora/objects/orbit:50538/datastreams/file_2802354/content.

PINSON P, SIEBERT N, KARINIOTAKIS G. Forecasting of regional wind generation by a dynamic fuzzy-neural network based upscaling approach [EB/OL]. [2013-03-01]. http:∥hal.archires-ouvertes.fr/docs/00/53/05/50/PDF/fiche_00530550.pdf.

SIEBERT N. Development of methods for regional wind power prediction [D]. Paris, France: Ecole des Mines de Paris, 2008.

LOBO M G, SÁNCHEZ I. Regional wind power forecasting based on smoothing techniques, with application to the Spanish peninsular system [J]. IEEE Transactions on Power Systems, 2012, 27(4): 1990-1997.

白永祥, 房大中, 侯佑华, 等. 内蒙古电网区域风电功率预测系统 [J]. 电网技术, 2010, 34(10): 157-162.

BAI Yongxiang, FANG Dazhong, HOU Youhua, et al. Regional wind power forecasting system for Inner Mongolia power grid [J]. Power System Technology, 2010, 34(10): 157-162.

任俊龙, 王海霞, 吕泉, 等. 一种区域风电功率预测模式的研究 [C]∥中国高等学校电力系统及其自动化专业第28届学术年会. 北京: 中国电机工程学会, 2012: A-1023.

于炳霞, 王知嘉, 周海, 等. 基于主成分分析方法的区域功率预测参数确定算法研究 [EB/OL]. [2013-02-22] http:∥d.g.wanfangdata.com.cn/Conference_7578030.aspx.

陈倩. 区域风电功率一体化预测方法研究 [D]. 石家庄: 华北电力大学, 2010.

SIEBERT N, KARINIOTAKIS G. Reference wind farm selection for regional wind power prediction models [C]∥European Wind Energy Conference and Exhibition. Brussels, Belgium: European Wind Energy Association, 2006: 1928-1937.

别朝红, 贺旭伟, 安佳坤, 等. 一种基于时空分布特性的区域风电功率预测方法: 中国, CN 102570449A [P]. 2012-07-11.

田红亮,赵春华,朱大林,等.整个螺栓结合部的法向连接动刚度及试验验证.2012,46(9):31-36.[doi:10.7652/xjtuxb 201209007]

曲翀,王秀丽,谢绍宇,等.含风电电力系统随机生产模拟的改进算法.2012,46(6):115-121. [doi:10.7652/xjtuxb2012 06020]

程临燕,张保会,李光辉,等.采用有向电气介数的脆弱线路选取.2011,45(6):91-96.[doi:10.7652/xjtuxb201106017]

赵攀,戴义平,夏俊荣,等.卡尔曼滤波修正的风电场短期功率预测模型.2011,45(5):47-51.[doi:10.7652/xjtuxb2011 05009]

岳涵,赵登福,夏道止,等.求解大型电力系统机电振荡模态的逆幂方法.2010,44(10):93-98. [doi:10.7652/xjtuxb2010 10018]

褚建峰,王曙鸿,邱捷,等.新型桥式高温超导故障限流器的设计.2010,44(10):99-104.[doi:10.7652/xjtuxb201010019]

谷永刚,肖凯,夏经德,等.利用增量二次规划和启发式方法的电力系统动态无功优化.2010,44(8):106-111. [doi:10.7652/xjtuxb201008021]

程松,周文华,赵登福,等.短期负荷预测的样本动态组织方法.2009,43(4):95-100.[doi:10.7652/xjtuxb200904022]

汪成根,张保会,李鹏,等.基于自适应解列的电力系统解列面快速搜索.2009,43(2):90-95. [doi:10.7652/xjtuxb2009 02020]

郑睿敏,李建华,李作红,等.考虑尾流效应的风电场建模以及随机潮流计算.2008,42(12):1515-1520.[doi:10.7652/xjtuxb200812017]

别朝红,刘辉,李甘,等.含风电场电力系统电压波动的随机潮流计算与分析.2008,42(12):1500-1505. [doi:10.7652/xjtuxb200812014]

别朝红,周婷,王锡凡.电力系统多时段无功优化研究.2008,42(6):698-702.[doi:10.7652/xjtuxb200806011]

徐林,王秀丽,王锡凡,等.电力系统充裕度分析中的快速组合事件概率算法.2008,42(6):713-717. [doi:10.7652/xjtuxb200806014]

李静耘,杜正春,楚国莉,等.基于聚类的多运行方式下电力系统稳定器设计.2008,42(2):204-208. [doi:10.7652/xjtuxb200802017]

王云,张伏生,陈建斌,等.电力系统多目标无功优化研究.2008,42(2):213-217.[doi:10.7652/xjtuxb200802019]

丁晓莺,王锡凡,王建学.一种考虑可靠性指标的最优潮流模型.2007,41(10):1219-1223. [doi:10.7652/xjtuxb2007 10021]

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