基于功率波动过程的风电功率短期预测及误差修正
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摘要
风资源因具有较强的波动性、随机性与间断性等特点而导致风电功率预测精度不高。为减小风电功率波动对电网的冲击,提高电力系统对风电的接受与消纳能力,提出了改进的风电功率短期预测方法与基于波动的误差修正方法。首先将风电功率按不同波动过程进行聚类划分,提取不同波动的特征曲线对功率值进行修正;采用引力搜索算法优化的反向传播神经网络(GSA-BP)作为基本预测方法进行预测;分析不同波动过程下的预测误差表现,建立预测误差与综合气象指标的映射关系。针对不同波动过程建立相应的风电功率误差修正模型,提出了线性模型和GSA-BP非线性模型相结合的方式对预测误差进行修正,最后以功率预测值叠加预测误差修正值作为最终预测结果。该风电功率预测误差修正方法不仅涉及风速风向等常规因素,而且考虑到了风电功率的波动性。
Wind resources have the characteristics of strong fluctuation,randomness and discontinuity,which lead to the low accuracy of wind power forecasting.In order to reduce the impact of wind power fluctuations on the power grid and improve the ability of power systems to accept and absorb wind power,an improved wind power short-term prediction method and fluctuation based error correction method are proposed.Firstly,the wind power is divided into clusters according to different fluctuation processes.The characteristic curves of different fluctuations are extracted to correct the power values.Secondly,the back propagation neural network optimized by gravitational search algorithm(GSA-BP)is used as the basic prediction method to predict.Then the performance of forecasting errors under different fluctuations is analyzed,and the mapping relationship between forecasting errors and comprehensive meteorological indicators is established.A corresponding wind power error correction model is established for different fluctuation processes.A combination of linear model and GSA-BP nonlinear model is proposed to modify the prediction error.Finally,the power prediction value is added to the prediction error correction value as the final forecast result.The wind power prediction error correction method not only involves conventional factors such as wind speed and direction,but also takes into account the fluctuation of wind power.
Wind resources have the characteristics of strong fluctuation,randomness and discontinuity,which lead to the low accuracy of wind power forecasting.In order to reduce the impact of wind power fluctuations on the power grid and improve the ability of power systems to accept and absorb wind power,an improved wind power short-term prediction method and fluctuation based error correction method are proposed.Firstly,the wind power is divided into clusters according to different fluctuation processes.The characteristic curves of different fluctuations are extracted to correct the power values.Secondly,the back propagation neural network optimized by gravitational search algorithm(GSA-BP)is used as the basic prediction method to predict.Then the performance of forecasting errors under different fluctuations is analyzed,and the mapping relationship between forecasting errors and comprehensive meteorological indicators is established.A corresponding wind power error correction model is established for different fluctuation processes.A combination of linear model and GSA-BP nonlinear model is proposed to modify the prediction error.Finally,the power prediction value is added to the prediction error correction value as the final forecast result.The wind power prediction error correction method not only involves conventional factors such as wind speed and direction,but also takes into account the fluctuation of wind power.
引文
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[18]周文全.BP网络泛化能力的增强改进[D].广州:暨南大学,2012.ZHOU Wenquan.The improvement of BP network generalization ability[D].Guangzhou:Jinan University,2012.
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[21]MOGHRAM I,RAHMAN S.Analysis and evaluation of five short-term load forecasting techniques[J].IEEE Transactions on Power Systems,1989,4(4):1484-1491.
[22]杨红英,冯双磊,王勃,等.基于线性回归的风电功率预测误差修正方法[J].电力系统及其自动化学报,2013,25(4):14-17.YANG Hongying,FENG Shuanglei,WANG Bo,et al.Study of the MOS method based on linear regression for wind power prediction[J].Proceedings of the CSU-EPSA,2013,25(4):14-17.
[23]郁琛,薛禹胜,文福拴,等.按时序特征优化模型后在线选配的超短期风电预测[J].电力系统自动化,2015,39(8):5-11.DOI:10.7500/AEPS20141230007.YU Chen,XUE Yusheng,WEN Fushuan,et al.An ultrashort-term wind power prediction method using“offline classification and optimization online model”based on time series features[J].Automation of Electric Power Systems,2015,39(8):5-11.DOI:10.7500/AEPS20141230007.
[2]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.DOI:10.7500/AEPS20130911004.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.DOI:10.7500/AEPS20130911004.
[3]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.
[4]薛禹胜,郁琛,赵俊华,等.关于短期及超短期风电功率预测的评述[J].电力系统自动化,2015,39(6):141-151.DOI:10.7500/AEPS20141218003.XUE Yusheng,YU Chen,ZHAO Junhua,et al.A review on short-term and ultra-short-term wind power prediction[J].Automation of Electric Power Systems,2015,39(6):141-151.DOI:10.7500/AEPS20141218003.
[5]解陈力.基于风电出力特性和Copula理论的风电时间序列生成[D].武汉:华中科技大学,2015.XIE Chenli.Wind power time series generation based on wind power characteristics and Copula[D].Wuhan:Huazhong University of Science and Technology,2015.
[6]黄坡,朱小帆,查晓明,等.基于波动过程聚类的风电功率预测极大误差估计方法[J].电力系统保护与控制,2016,44(13):130-136.HUANG Po,ZHU Xiaofan,ZHA Xiaoming,et al.An estimation method for wind power prediction great error based on clustering fluctuation process[J].Power System Protection and Control,2016,44(13):130-136.
[7]CHEN Daojun,GUO Hu,ZUO Jian,et al.Research on modeling method describing wind power fluctuation probability density based on nonparametric kernel density estimation[C]//2016 3rd International Conference on Intelligent Green Building and Smart Grid,April 22-25,2018,Taiwan,China:178-187.
[8]阎洁,许成志,刘永前,等.基于风速云模型相似日的短期风电功率预测方法[J].电力系统自动化,2018,42(6):53-59.DOI:10.7500/AEPS20170605001.YAN Jie,XU Chengzhi,LIU Yongqian,et al.Short-term wind power prediction method based on wind speed cloud model in similar day[J].Automation of Electric Power Systems,2018,42(6):53-59.DOI:10.7500/AEPS20170605001.
[9]史坤鹏,乔颖,赵伟,等.计及历史数据熵关联信息挖掘的短期风电功率预测[J].电力系统自动化,2017,41(3):13-18.DOI:10.7500/AEPS20160504020.SHI Kunpeng,QIAO Ying,ZHAO Wei,et al.Short-term wind power prediction based on entropy association information mining of historical data[J].Automation of Electric Power Systems,2017,41(3):13-18.DOI:10.7500/AEPS20160504020.
[10]TENG Qijun,WANG Chengfu,LIANG Jun,et al.Modeling the temporal correlation of ultra-short term wind power forecast error based on Copula theory[C]//2017 IEEEConference on Energy Internet and Energy System Integration,November 26-28,2017,Beijing,China:1-4.
[11]欧阳庭辉,查晓明,秦亮,等.基于相似性修正的风电功率爬坡事件预测方法[J].中国电机工程学报,2017,37(2):572-580.OUYANG Tinghui,ZHA Xiaoming,QIN Liang,et al.Wind power ramps prediction method based on amendment of similar events[J].Proceedings of the CSEE,2017,37(2):572-580.
[12]茆美琴,曹雨,周松林.基于误差叠加修正的改进短期风电功率预测方法[J].电力系统自动化,2013,37(23):34-38.MAO Meiqin,CAO Yu,ZHOU Songlin.Improved short-term wind power forecasting method based on accumulative error correction[J].Automation of Electric Power Systems,2013,37(23):34-38.
[13]王东风,王富强,牛成林.小波分解层数及其组合分量对短期风速多步预测的影响分析[J].电力系统保护与控制,2014,42(8):82-89.WANG Dongfeng,WANG Fuqiang,NIU Chenglin.Analysis of wavelet decomposition for multi-step prediction of shortterm wind speed[J].Power System Protection and Control,2014,42(8):82-89.
[14]李驰,刘纯,黄越辉,等.基于波动特性的风电出力时间序列建模方法研究[J].电网技术,2015,39(1):208-214.LI Chi,LIU Chun,HUANG Yuehui,et al.Study on the modeling method of wind power time series based on fluctuation characteristics[J].Power System Technology,2015,39(1):208-214.
[15]董立文,范澍.基于自组织映射网络的峰值负荷预测方法[J].中国电力,2007,40(8):32-35.DONG Liwen,FAN Shu.Peak load forecasting using the selforganizing map[J].Electric Power,2007,40(8):32-35.
[16]朱倩雯,叶林,赵永宁,等.风电场输出功率异常数据识别与重构方法研究[J].电力系统保护与控制,2015,43(3):38-45.ZHU Qianwen,YE Lin,ZHAO Yongning,et al.Methods for elimination and reconstruction of abnormal power data in wind farms[J].Power System Protection and Control,2015,43(3):38-45.
[17]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.Ashort-term wind power prediction method based on wavelet decomposition and BP neural network[J].Automation of Electric Power Systems,2011,35(16):44-48.
[18]周文全.BP网络泛化能力的增强改进[D].广州:暨南大学,2012.ZHOU Wenquan.The improvement of BP network generalization ability[D].Guangzhou:Jinan University,2012.
[19]HASANIEN H M.Gravitational search algorithm-based optimal control of archimedes wave swing-based wave energy conversion system supplying a DC microgrid under uncertain dynamics[J].IET Renewable Power Generation,2017,6(11):63-770.
[20]江滢,罗勇,赵宗慈.中国未来风功率密度变化预估[J].资源科学,2010,32(4):640-649.JIANG Ying,LUO Yong,ZHAO Zongci.Projection of wind power density in China in the 21st century by climate models[J].Resources Science,2010,32(4):640-649.
[21]MOGHRAM I,RAHMAN S.Analysis and evaluation of five short-term load forecasting techniques[J].IEEE Transactions on Power Systems,1989,4(4):1484-1491.
[22]杨红英,冯双磊,王勃,等.基于线性回归的风电功率预测误差修正方法[J].电力系统及其自动化学报,2013,25(4):14-17.YANG Hongying,FENG Shuanglei,WANG Bo,et al.Study of the MOS method based on linear regression for wind power prediction[J].Proceedings of the CSU-EPSA,2013,25(4):14-17.
[23]郁琛,薛禹胜,文福拴,等.按时序特征优化模型后在线选配的超短期风电预测[J].电力系统自动化,2015,39(8):5-11.DOI:10.7500/AEPS20141230007.YU Chen,XUE Yusheng,WEN Fushuan,et al.An ultrashort-term wind power prediction method using“offline classification and optimization online model”based on time series features[J].Automation of Electric Power Systems,2015,39(8):5-11.DOI:10.7500/AEPS20141230007.