全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...
电网技术  2015 

非迭代与分时段最优的风电功率短期预测

DOI: 10.13335/j.1000-3673.pst.2015.10.013, PP. 2766-2771

Keywords: 风电功率,短期预测,非迭代,分时段最优

Full-Text   Cite this paper   Add to My Lib

Abstract:

风电功率短期预测模型多数以数值天气预报信息(numericalweatherprediction,NWP)为输入,然而NWP数据存在一定的局限性。且以历史统计数据为输入时,误差会随预测提前时间的增大而急剧增加,因此常应用于超短期预测。为此提出了一种非迭代-分时段最优预测模型,首先以历史数据为输入,采用非迭代方式预测未来24h的风电功率。然后找出分别使各个预测时段误差最小的最优输入个数,并求得基于历史风速数据和历史功率数据2种模型的分时段最优权重。实验证明,非迭代-分时段最优模型有效地消除了累积误差增大了预测范围,大大提高了各个时段的预测精度。与其他预测模型相比,该模型数据来源方便、结构简单、预测精度高。

References

[1]  丁华杰,宋永华,胡泽春,等.基于风电场功率特性的日前风电预测误差概率分布研究[J].中国电机工程学报,2013,33(34):136-144.Ding Huajie,Song Yonghua,Hu Zechun,et al.Probability density function of day-ahead wind power forecast errors based on power curves of wind farms[J].Proceedings of the CSEE,2013,33(34):136-144(in Chinese).
[2]  Bossanyi E A.Short-term wind prediction using Kalman filter[J].Wind Engineering,1985,9(1):1-8.
[3]  潘迪夫,刘辉,李燕飞.基于时间序列分析和卡尔曼滤波算法的风电场风速预测优化模型[J].电网技术,2008,32(7):82-86.Pan Difu,Liu Hui,Li Yanfei.A wind speed forecasting optimization model for wind farms based on time series analysis and Kalman filter algorithm[J].Power System Technology,2008,32(7):82-86(in Chinese).
[4]  蔡凯,谭伦农,李春林,等.时间序列与神经网络法相结合的短期风速预测[J].电网技术,2008,32(8):82-85.Cai Kai,Tan Lunnong,Li Chunlin,et al.Short-term wind speed forecasting combing time series and neural network method[J].Power System Technology,2008,32(8):82-85(in Chinese).
[5]  Kamal L,Jafri Y Z.Time series models to simulate and forecast hourly averaged wind speed in Wuetta Pakistan[J].Solar Energy,1997,61(1):23-32.
[6]  吴俊利,张步涵,王魁.基于Adaboost的BP神经网络改进算法在短期风速预测中的应用[J].电网技术,2012,36(9):221-225.Wu Junli,Zhang Buhan,Wang Kui.Application of Adaboost-based BP neural network for short-term wind speed forecast[J].Power System Technology,2012,36(9):221-225 (in Chinese).
[7]  师洪涛,杨静玲,丁茂生,等.基于小波-BP神经网络的短期风电功率预测方法[J].电力系统自动化,2011,35(16):44-48.Shi Hongtao,Yang Jingling,Ding Maosheng,et al.A short-term wind power prediction method based on wavelet decomposition and BP neural network[J].Automation of Electric Power Systems,2011,35(16):44-48(in Chinese).
[8]  杨琦,张建华,王向峰,等.基于小波-神经网络的风速及风力发电量预测[J].电网技术,2009,33(17):44-48.Yang Qi,Zhang Jianhua,Wang Xiangfeng,et al.Wind speed and generated wind power forecast based on wavelet-neural network[J].Power System Technology,2009,33(17):44-48(in Chinese).
[9]  曾杰,张华.基于最小二乘支持向量机的风速预测模型[J].电网技术,2009,33(18):144-147.Zeng Jie,Zhang Hua.A wind speed forecasting model based on least squares support vector machine[J].Power System Technology,2009,33(18):144-147(in Chinese).
[10]  杜颖,卢继平,李青,等.基于最小二乘支持向量机的风电场短期风速预测[J].电网技术,2008,32(15):61-66.Du Ying,Lu Jiping,Li Qing,et al.Short-term wind speed forecasting of wind farm based on least square-support vector machine[J].Power System Technology,2008,32(15):61-66 (in Chinese).
[11]  叶林,刘鹏.基于经验模态分解和支持向量机的短期风电功率组合预测模型[J].中国电机工程学报,2011,31(31):102-108.Ye Lin,Liu Peng.Combined model based on EMD-SVM for short-term wind power prediction[J].Proceedings of the CSEE,2011,31(31):102-108(in Chinese).
[12]  陈宁,沙倩,汤奕,等.基于交叉熵理论的风电功率组合预测方法[J].中国电机工程学报,2012,32(4):29-34.Chen Ning,Sha Qian,Tang Yi,et al.A combination method for wind power predication based on cross entropy theory[J].Proceedings of the CSEE,2012,32(4):29-34(in Chinese).
[13]  李莉,刘永前,杨勇平,等.基于CFD流场预计算的短期风速预测方法[J].中国电机工程学报,2013,33(7):27-32.Li Li,Liu Yongqian,Yang Yongping,et al.Short-term wind speed forecasting based on CFD pre-calculated flow fields[J].Proceedings of the CSEE,2013,33(7):27-32(in Chinese).
[14]  Chen N,Qian Z,Nabney I T,et al.Wind power forecasts using Gaussian processes and numerical weather prediction[J].IEEE Transactions on Power Systems,2014,29(2):656-665.
[15]  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(in Chinese).
[16]  Vargas L,Paredes G,Bustos G.Data mining techniques for very short term prediction of wind power[C]//Bulk Power System Dynamics and Control (IREP)-VIII (IREP),2010 IREP Symposium.Rio de Janeiro:IEEE,2010:1-7.
[17]  王焱,汪震,黄民翔,等.基于OS-ELM和Bootstrap方法的超短期风电功率预测[J].电力系统自动化,2014,38(6):14-19.Wang Yan,Wang Zhen,Huang Minxiang,et al.Ultra-short-term wind power prediction based on OS-ELM and bootstrap method[J].Automation of Electric Power Systems,2014,38(6):14-19(in Chinese).
[18]  Bhaskar K,Singh S N.AWNN-assisted wind power forecasting using feed-forward neural network[J].IEEE Transactions on Sustainable Energy,2012,3(2):306-315.
[19]  王晓兰,王明伟.基于小波分解和最小二乘支持向量机的短期风速预测[J].电网技术,2010,34(1):179-184.Wang Xiaolan,Wang Mingwei.Short-term wind speed forecasting based on wavelet decomposition and least square support vector machine[J].Power System Technology,2010,34(1):179-184(in Chinese).
[20]  武小梅,白银明,文福拴.基于RBF神经元网络的风电功率短期预测[J].电力系统保护与控制,2011,39(15):80-83.Wu Xiaomei,Bai Yinming,Wen Fushuan.Short-term wind power forecast based on the radial basis function neural network[J].Power System Protection and Control,2011,39(15):80-83(in Chinese).

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133