基于自适应的动态三次指数平滑法的风电场风速预测
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摘要
随着风力发电的快速发展,对风电场的风速实现较准确的预测也逐步成为风电领域研究的热点。为了提高风速的预测精度,综合考虑风速历史时间序列的影响,在传统的三次指数平滑方法的基础上,提出了一种自适应的动态三次指数平滑方法来进行风速预测。该方法利用了地毯式搜索方法,根据误差平方和最小的原则及时调整并获得最佳的平滑系数,然后进行后续的一步或多步风速预测。通过与传统的三次指数平滑法、灰色模型预测法比较,验证了自适应的动态三次指数平滑法在风电场风速预测中的准确性和高效性。
With the rapid development of wind power in recent years, to achieve more accurate predictions of wind speed on wind farm has gradually become an hot issue in research. To improve the wind speed forecasting accuracy for wind farm, a new self-adaptive and dynamic forecasting method is presented, which is based on the wind speed time series and traditional cubic exponential smoothing(ES) method. According to the principle of minimum error sum of squares, the smoothing factor is chosen with blanket search method and changed in time. Then one-step or multistep wind speed forecasting can be done with the new method. By comparing with the traditional cubic exponential smoothing method and gray model forecasting method, it can be proved that the self-adaptive and dynamic forecasting method in wind speed forecasting is accurate and efficient.
With the rapid development of wind power in recent years, to achieve more accurate predictions of wind speed on wind farm has gradually become an hot issue in research. To improve the wind speed forecasting accuracy for wind farm, a new self-adaptive and dynamic forecasting method is presented, which is based on the wind speed time series and traditional cubic exponential smoothing(ES) method. According to the principle of minimum error sum of squares, the smoothing factor is chosen with blanket search method and changed in time. Then one-step or multistep wind speed forecasting can be done with the new method. By comparing with the traditional cubic exponential smoothing method and gray model forecasting method, it can be proved that the self-adaptive and dynamic forecasting method in wind speed forecasting is accurate and efficient.
引文
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[2]刘烨,卢小芬,方瑞明,等.风力发电系统中风速预测方法综述[J].电网与清洁能源,2010,26(6):62-66.LIU Ye,LU Xiao-fen,FANG Rui-ming,et al.A review on wind speed forecast methods in wind power system[J].Power System and Clean Energy,2010,26(6):62-66.
[3]孙元章,吴俊,李国杰.基于风速预测和随机规划的含风电场电力系统动态经济调度[J].中国电机工程学报,2009,29(4):41-47.SUN Yuan-zhang,WU Jun,LI Guo-jie.Dynamic economic dispatch considering wind power penetration based on wind speed forecasting and stochastic programming[J].Proceedings of the CSEE,2009,29(4):41-47.
[4]邵璠,孙育河,梁岚珍.基于ARMA模型的风电场风速短期预测[J].电网与清洁能源,2008,24(7):52-55.SHAO Fan,SUN Yu-he,LIANG Lan-zhen.Wind speed short-term forecast for wind farms based on ARMA model[J].Power System and Clean Energy,2008,24(7):52-55.
[5]李俊芳,张步涵,谢光龙,等.基于灰色模型的风速-风电功率预测研究[J].电力系统保护与控制,2010,38(19):151-159.LI Jun-fang,ZHANG Bu-han,XIE Guang-long,et al.Grey predictor models for wind speed-wind power prediction[J].Power System Protection and Control,2010,38(19):151-159.
[6]戴浪,黄守道,黄科元,等.风电场风速的神经网络组合预测模型[J].电力系统及其自动化学报,2011,23(4):27-31.DAI Lang,HUANG Shou-dao,HUANG Ke-yuan,et al.Combination forecasting model based on neural networks for wind speed in wind farm[J].Proceedings of the CSU-EPSA,2011,23(4):27-31.
[7]杨江平.基于神经网络组合预测的风电场风速及发电功率短期预测[D].重庆:重庆大学,2012.YANG Jiang-ping.Short-term wind speed and power forecasting in wind farm based on ANN combination forecasting[D].Chongqing:Chongqing University,2012.
[8]杨秀媛,肖洋,陈树勇.风电场风速和发电功率预测研究[J].中国电机工程学报,2005,25(11):1-5.YANG Xiu-yuan,XIAO Yang,CHEN Shu-yong.Wind speed and generated power forecasting in wind farm[J].Proceedings of the CSEE,2005,25(11):1-5.
[9]王韶,杨江平,李逢兵,等.基于经验模式分解和神经网络的短期风速组合预测[J].电力系统保护与控制,2012,40(10):6-12.WANG Shao,YANG Jiang-ping,LI Feng-bing,et al.Short-term wind speed forecasting based on EMD and ANN[J].Power System Protection and Control,2012,40(10):6-12.
[10]孙国强,卫志农,翟玮星.基于RVM与ARMA误差校正的短期风速预测[J].电工技术学报,2012,27(8):187-193.SUN Guo-qiang,WEI Zhi-nong,ZHAI Wei-xing.Short term wind speed forecasting based on RVM and ARMA error correcting[J].Transactions of China Electrotechnical Society,2012,27(8):187-193.
[11]齐雪雯,谢开贵,胡博.风速分布预测的指数平滑模型及其在配电网可靠性中的应用[J].电力系统保护与控制,2012,40(12):1-6.QI Xue-wen,XIE Kai-gui,HU Bo.Exponential smoothing model for wind speed distribution forecast and its application in electrical distribution network[J].Power System Protection and Control,2012,40(12):1-6.
[12]李颖.时间序列指数平滑算法的改进研究[D].阜新:辽宁工程技术大学,2009.LI Ying.Improvement research of time series exponential smoothing[D].Fuxin:Liaoning Technical University,2009.
[13]张宁宇,高山,赵欣.一种考虑风电随机性的机组组合模型及其算法[J].电工技术学报,2012,37(7):50-53.ZHANG Ning-yu,GAO Shan,ZHAO Xin.A unit commitment model and algorithm with randomness of wind power[J].Transactions of China Electrotechnical Society,2012,37(7):50-53.
[14]罗文,王莉娜.风电场短期风速预测研究[J].电工技术学报,2011,26(7):68-74.LUO Wen,WANG Li-na.Short-term wind speed forecasting for wind farm[J].Transactions of China Electrotechnical Society,2011,26(7):68-74.
[15]刘永前,韩爽,胡永生.风电场出力短期预报研究综述[J].现代电力,2007,24(5):6-11.LIU Yong-qian,HAN Shuang,HU Yong-sheng.Review on short-term wind power prediction[J].Modern Electric Power,2007,24(5):6-11.