[1]徐梅梅,任祖怡,陈建国,等.基于空间相关性和小波-神经网络的短期风 电功率预测模型[J].南京理工大学学报(自然科学版),2016,40(03):360.[doi:10.14177/j.cnki.32-1397n.2016.40.03.019]
 Xu Meimei,Ren Zuyi,Chen Jianguo,et al.Short-term wind power forecasting model based on spatial correlation and wavelet-neural network[J].Journal of Nanjing University of Science and Technology,2016,40(03):360.[doi:10.14177/j.cnki.32-1397n.2016.40.03.019]
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基于空间相关性和小波-神经网络的短期风 电功率预测模型
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
40卷
期数:
2016年03期
页码:
360
栏目:
出版日期:
2016-06-30

文章信息/Info

Title:
Short-term wind power forecasting model based on spatial correlation and wavelet-neural network
文章编号:
1005-9830(2016)03-0360-06
作者:
徐梅梅1任祖怡2陈建国1倪建军2张俊芳3宁 楠4赵继伟4
1.贵州电网有限责任公司 电力科学研究院,贵州 贵阳 550002; 2.南京南瑞继保电气有限公司,江苏 南京 211102; 3.南京理工大学 自动化学院,江苏 南京 210094; 4.贵州电网有限责任公司 六盘水供电局,贵州 六盘水 553000
Author(s):
Xu Meimei1Ren Zuyi2Chen Jianguo1Ni Jianjun2Zhang Junfang3 Ning Nan4Zhao Jiwei4
1.Electric Power Research Institute of Guizhou Power Grid Co.,Ltd.,Guiyang 550002,China; 2.NR Electric Co.,Ltd.,Nanjing 211102,China; 3.School of Automation,Nanjing University of Science and Technology,Nanjing 210094,China; 4.Liupanshui Power Supply Bu
关键词:
空间相关性 小波-神经网络 风电功率预测 小波基函数 逆传播神经网络 风能利用
Keywords:
spatial correlation wavelet-neural network wind power forecasting wavelet basis function back propagation neural network wind energy utilization
分类号:
TM614
DOI:
10.14177/j.cnki.32-1397n.2016.40.03.019
摘要:
为准确预测风电功率,该文提出1种预测模型。利用风速空间相关性把握风速时间序列的变化规律。将小波基函数植入神经网络的神经元节点中作为传递函数,对风电功率进行预测。对2相邻风电场短期风电功率预测算例进行仿真与对比分析。结果表明基于空间相关性和小波-神经网络(SC-WNN)的预测模型与逆传播神经网络(BPNN)和小波-神经网络(WNN)预测模型相比,平均百分比误差最大降低了0.164 3。
Abstract:
In order to predict wind power accurately,a prediction model is proposed here.The inherent law of wind speed time series is extracted by the wind speed spatial correlation.The wavelet basis function is transferred into the neutron nodes of the neural network as the transfer function,and the wind power is predicted.The short-term wind power forecasting examples of two adjacent wind farms are simulated and analyzed.The simulation results show that compared with the back propagation neural network(BPNN)and wavelet-neural network(WNN)prediction models,the average percentage error of SC-WNN prediction model is reduced by 0.164 3.

参考文献/References:

[1] 孟令斌,朱凤龙.混合电源及功率预测系统在风电并网中的应用[J].电力系统保护与控制,2015,43(13):79-85.
Meng Lingbin,Zhu Fenglong.Applications of the mixing power and power forecasting system in wind power[J].Power System Protection and Control,2015,43(13):79-85.
[2]冯凯,应展烽,吴军基,等.基于小波包变换和峰式马尔科夫链的风速短期预测[J].南京理工大学学报,2014,38(5):639-643,657.
Feng Kai,Ying Zhanfeng,Wu Junji,et al.Short-term wind speed forecast based on wavelet packet decomposition and peak-type Markov chain[J].Journal of Nanjing University of Science and Technology,2014,38(5):639-643,657.
[3]周松林,茆美琴,苏建徽.风电功率短期预测及非参数区间估计[J].中国电机工程学报,2011,31(25):10-16.
Zhou Songlin,Mao Meiqin,Su Jianhui.Short-term forecasting of wind power and non-parametric confidence interval estimation[J].Proceedings of the CSEE,2011,31(25):10-16.
[4]范高锋,王伟胜,刘纯,等.基于人工神经网络的风电功率预测[J].中国电机工程学报,2008,28(34):118-123.
Fan Gaofeng,Wang Weisheng,Liu Chun,et al.Wind power forecasting based on artificial neural networks[J].Proceedings of the CSEE,2008,28(34):118-123.
[5]何东,刘瑞叶.基于主成分分析的神经网络动态集成风功率超短期预测[J].电力系统保护与控制,2013,41(4):50-54.
He Dong,Liu Ruiye.Ultra-short-term wind power prediction using ANN ensemble based on the principal components analysis[J].Power System Protection and Control,2013,41(4):50-54.
[6]孟安波,陈育成.基于虚拟预测与小波包变换的风电功率组合预测[J].电力系统保护与控制,2014,42(3):71-76.
Meng Anbo,Chen Yucheng.Wind power combination forecasting based on wavelet packet transform and virtual forecasting method[J].Power System Protection and Control,2014,42(3):71-76.
[7]张学清,梁军,张熙,等.基于样本熵和极端学习机的超短期风电功率组合预测模型[J].中国电机工程学报,2013,33(25):33-40.
Zhang Xueqing,Liang Jun,Zhang Xi,et al.Combined model for ultra short-term wind power prediction based on sample entropy and extreme learning machine[J].Proceedings of the CSEE,2013,33(25):33-40.
[8]张露,卢继平,梅亦蕾,等.基于不同优化准则的风电功率预测[J].电力自动化设备,2015,35(5):139-145.
Zhang Lu,Lu Jiping,Mei Yilei,et al.Wind power forecasting based on different optimization criterions[J].Electric Power Automation Equipment,2015,35(5):139-145.
[9]李文良,卫志农,孙国强,等.基于改进空间相关法和径向基神经网络的风电场短期风速分时预测模型[J].电力自动化设备,2009,29(6):89-92.
Li Wenliang,Wei Zhinong,Sun Guoqiang,et al.Multi-interval wind speed forecast model based on improved spatial correlation and RBF neural network[J].Electric Power Automation Equipment,2009,29(6):89-92.
[10]Bechrakis D A,Sparis P D.Correlation of wind speed between neighboring measuring stations[J].IEEE Trans on Energy Conversion,2004,19(2):400-406.
[11]Sahin A D,Sen Z.Wind energy directional spatial correlation functions and application for prediction[J].Wind Engineering,2000,24(3):223-231.
[12]潘玉民,邓永红,张全柱.小波神经网络模型的确定性预测及应用[J].计算机应用,2013,33(4):1001-1005.
Pan Yumin,Deng Yonghong,Zhang Quanzhu.Deterministic prediction of wavelet neural network model and its application[J].Journal of Computer Applications,2013,33(4):1001-1005.
[13]陈妮亚,钱政,孟晓风,等.基于空间相关法的风电场风速多步预测模型[J].电工技术学报,2013,28(5):15-21.
Chen Niya,Qian Zheng,Meng Xiaofeng,et al.Multi-step ahead wind speed forecasting model based on spatial correlation and support vector machine[J].Transactions of China Electrotechnical Society,2013,28(5):15-21.
[14]叶林,赵永宁.基于空间相关性的风电功率预测研究综述[J].电力系统自动化,2014,38(14):126-135.
Ye Lin,Zhao Yongning.A review on wind power prediction based on spatial correlation approach[J].Automation of Electric Power Systems,2014,38(14):126-135.
[15]Bilgili M,Sahin B,Yasar A.Application of artificial neural networks for the wind speed prediction of target station using reference stations data[J].Renewable Energy,2007,32(14):2350-2360.
[16]Foley A M,Leahy P G,Marvuglia A,et al.Current methods and advances in forecasting of wind power generation[J].Renewable Energy,2012,37(1):1-8.

备注/Memo

备注/Memo:
收稿日期:2016-03-05 修回日期:2016-05-08
基金项目:国家科技支撑计划(2013BAA02B02)
作者简介:徐梅梅(1986-),女,硕士,工程师,主要研究方向:电力系统仿真分析,E-mail:xumeimei_1986@163.com; 通讯作者:任祖怡(1972-),男,研究员级高级工程师,主要研究方向:电力系统稳定分析与控制,E-mail:renzy@nrec.com。
引文格式:徐梅梅,任祖怡,陈建国,等.基于空间相关性和小波-神经网络的短期风电功率预测模型[J].南京理工大学学报,2016,40(3):360-365.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2016-06-30