摘要
在能源危机日益严重的今天,新能源的开发利用受到越来越多的关注。在这些新能源中,风力发电更是得到了广泛应用。然而,风电功率的随机性,给电力系统运营带来了一系列问题,当大规模的风电场并入电网时,更是给电网的安全和稳定带来了极大的负担。为了解决该问题,提出了风电场风速和风电功率预测。其中风速预测为风电功率的间接预测方式。通过对风电场风速的预测,可以降低风电功率的随机性,从而可有效缓解风电场给电力系统造成的不利影响。风速预测近年来有一定发展,但是还存在许多不足之处,仍然具有很大的进步空间。
通过考虑多种风速短期预测模型,发现单一的风速预测模型的预测精度提升空间有限,于是根据对支持向量机和组合预测模型的研究,利用它们的算法优点,提出了基于一种改进的支持向量机——最小二乘支持向量机的组合预测模型。利用组合预测模型可综合各单项预测模型的信息,并且最小二乘支持向量机可以简化、优化组合预测模型。该组合模型采用RBF神经网络、BP神经网络和遗传神经网络的风速预测值作为输入,实际风速值作为输出,并建立了线性组合预测模型,且以此为参照来分析基于最小二乘支持向量机的组合预测模型的预测性能。各模型的预测性能,采用预测平均绝对误差、平均相对误差以及误差平方和三个误差指标来比较分析。以江西老爷庙的小时风速数据作为研究样本,运用MATLAB进行仿真,利用各模型对风速进行短期预测,证明了基于最小二乘支持向量机的风速组合预测模型的有效性。仿真试验表明,组合预测模型可进一步提升风速预测精度,而且相较于传统线性组合预测模型,基于最小二乘支持向量机的组合预测模型具有比较大的精度优势。
最后建立了基于MATLAB/GUI的风速预测系统,以便风速预测的进一步研究,通过该系统可以方便的调整参数、预测时段、模型类型,简化了复杂的调试过程,结果证明该系统相较于原始程序实现具有明显的优势,验证了风速预测系统的可行性。
Today with energy resource crisis becomes more severe, using and developing of new energy resource is attracting more and more attention. Among these renewable energy resources, wind power is widely used. However, the randomness of wind power brings a series of problems to power system operation. When large-scale wind farms are integrated to power grid, a great burden will act on the security and stability of power grid In order to resolve the problem, wind speed forecasting and wind power forecasting were proposed and wind speed forecasting is an indirect way to wind power forecasting. Through the forecasting of wind speed, the randomness of wind power will reduce, which can effectively alleviate the adverse effects on power system. In recent years, wind forecasting has made some progress, but there are also shortcomings, it has rooms for further research.
According to the consulted literatures and references, this paper reviewed and summarized the techniques on the short-term forecast model of wind speed, it was found that the precision of single forecast model needed to improve. Because of the advantages of support vector machines and combined forecast model, this paper presented a wind speed forecast model based on least square support vector machine. The combined forecast model which optimized by least square support vector machine could synthesize many kinds of message in single model. The forecasted wind speed which calculated by BP neural networks、RBF neural networks and genetic neural networks was separately set as the input of the combined model , the actual wind speed was set as the output of the combined model, then the forecast model was build based on the self-learning capability of the support vector machine. At the same time, this thesis elaborated the Linear combination forecast model which was used to analysis the performance of wind speed forecast model based on least square support vector machine. In order to compare the forecast performance of all kinds model, the average absolute deviation、average relative deviation、square deviation were used to analysis the forecast results. A simulation system was designed during MATLAB, the simulation results showed that the presented model could improve the accuracy of the wind speed forecast. The feasibility of the presented model was verified by the studied samples which the wind speeds per hour were in jiangxi Laoyemiao.
Finally, the wind speed forecasting system is established based on MATLAB/GUI to promote convenience on further study. The system can adjust the parameters of the model; setting predict times, selecting model and so on. Simulation results show that the system achieved obvious advantage compared to the original programs; it is proved the feasibility of wind forecasting system.
引文
[1]施鹏飞.从世界发展趋势展望我国风力发电前景.中国电力,2003,36(9):54-62
[2]李俊峰,高虎,王仲颖等.2008中国风电发展报告.北京:中国环境科学出版社,2008:1-19
[3]张新房,徐大平,吕跃刚等.风力发电技术的发展及若干问题.现代电力,2003,20(5):38-40
[4]曾杰.风电场短期风速预测模型和风力机动力分析:[华北电力大学硕士学位论文].北京:华北电力大学,2010,6-8
[5]赵新川.风力预测技术推进风电发展.高科技与产业化,2010,11(13):23-24
[6]雷亚洲,王伟胜,戴慧珠等.风电对电力系统运行价值分析.电网技术,2002,26(5):10-14
[7]翁文波.预测学.北京:石油工业出版社,1996,30-42
[8] Lexiadis M A,Dokopoulos P,Sahsamanoglou H S etal.Short term forecasting of wind speed and related electrical power.Solar Energy,1998,63(1):61-68
[9] Markridaks S.The art and Science of Forecasting.An assessment and Future Directions.IJF,1986,2(1):15-19
[10] Caixia Wang,Zongxiang Lu,Ying Qiao.Modeling of wind pattern and its application in wind speed forecasting.Sustainable Power Generation and Supply,2009,1-6
[11]王磊,姚恒申.时间序列和神经网络的组合预测及其应用.统计与决策,2005,19l (6):17-18
[12]吕涛,唐巍,所丽等.基于混沌相空间重构理论的风电场短期风速预测.电力系统保护与控制,2010,21(25):11-13
[13]胡代平.神经网络在预测中的一些应用研究.系统工程学报,1999,14(4):338-344
[14]孙雅明,张智晟.相空问重构和混沌神经网络融合的短期负荷预测研究.中国电机工程学报,2004,24(1):44-48
[15]范高锋,王伟胜,刘纯等.基于人工神经网络的风电功率预测.中国电机工程学报,2008,28(34):118-123
[16]熊信银,吴耀武.遗传算法及其在电力系统中的应用.武汉:华中科技大学出版社,2002,1-5
[17]赵菁,徐克明.神经网络和模糊理论在短期负荷预测中的应用.电力系统及其自动化学报,2010,22(3):129-133.
[18]张亚军,刘志刚,张大波.一种基于多神经网络的组合负荷预测模型.电网技术,2006,30(21):21-25.
[19]肖永山.基于神经网络的风电场风速时间序列预测研究.节能技术,2003,5(2):106-108
[20]杨秀媛,肖洋,陈树勇.风电场风速和发电功率预测研究.中国电机工程学报,2005, 25(11):1-5
[21]明伟.风电场短期风速预测研究:[兰州理工大学硕士学位论文].兰州:兰州理工大学,2009,10-11
[22] TarekH.E1-Fouly,Ehab F.El-Saadany,Magdy M.A.Salama.One Day Ahead Prediction of Wind Speed and Direction.IEEE Transactions on Energy Conversion,2008,23(1):l9l-201
[23]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.电网技术,2008,32(7):82-86.
[24]刘永前,韩爽,胡永生.风电场出力短期预报研究综述.现代电力,2007,24(90):6-11
[25] Peter Coppin,Jack Katzfey.The Feasibility of Wind Power Production Forecasting in the Australian Context.CSIRO Atmospheric Research.Report to:National Electricity Market Management Company Limited,2003,12(5):591-596
[26] Thanasis Gbarbounis,John B Theochairs,Minas C Alexiadis,et a1.Long-term wind speed and power forecasting using local recurrent Neural Network Models.IEEE Transactions on Energy Conversion,2006,21(1):273-284
[27]曹慧.支持向量机方法在风电场风度预测中的应用研究:[华北电力大学硕士学位论文].北京:华北电力大学,2010,16-21
[28]杜颖.基于最小二乘支持向量机的风电场短期风速预测.电网技术,2008,32(15):62-66.
[29]张疆涌.组合预测方研究:[东南大学硕士学位论文].南京:东南大学,2008:10-19
[30]李峰,刘静延,蒋录全.预测方法的发展及最新动态.情报杂志,2005,24(6):76-77
[31] Nourouz Kohzadi, Miltons Boyd. Neural Networks for forecasting: Anintroduction. Canadian Journal of Agricultural Economics, 1995, 43(3): 463-468
[32]唐小我.最优组合预测方法及其应用,数理统计与管理,1992,11(1):38-56
[33]邓自立,马建为,杜洪越.ARMA模型参数估计的两端最小二乘法.科学技术与工程,2002,2(5):3-6
[34]王慧勤.基于支持向量机的短期风速预测方法研究:[西安科技大学硕士论文].西安:西安科技大学,2009,7-12
[35]朱报春.基于模糊逻辑系统的组合预测方法及应用:[河海大学硕士学位论文].南京:河海大学,2003,13-25
[36]陈华友.组合预测方法及其应用研究:[博士学位论文].合肥:中国科学技术大学,2002,30-42
[37] Elliott D,Schwartz M,Scott G,etal.Wind energy resource atlas of southeast china.Golden,Colorado,USA:National Renewable Energy Laboratory, 2002:58-279
[38]叶杭冶.风力发电机组的控制技术(第2版).北京:机械工业出版社,2006,11-155
[39]陈国富.双馈感应风力发电机无速度传感器控制的仿真与实验.长沙:湖南大学,2010:1-5
[40]马洪飞,张薇,李伟伟等.基于直流电机的风力机模拟技术研究.太阳能学报,2007,28(11):1278-1283
[41]李晶,宋家骅,王伟胜.大型变速恒频风力发电机组建模与仿真.中国电机工程学报,2004,24(6):100-105
[42]李晶,方勇,宋家桦等.变速恒频双馈风电机组分段分层控制策略的研究.电网技术,2005,29(9):15-21
[43]赵仁德,贺益康,黄科元等.变速恒频风力发电机用交流励磁电源的研究.电工技术学报,2004,19(6):1-6
[44]吴国祥,黄建明,陈国呈等.变速恒频双馈风力发电运行综合控制策略.电机与控制学报,2008,12(4):435-441
[45] Cao Lei, Li Ran.Short-term wind speed forecasting model for wind farm based on wavelet decomposition. Electric utility deregulation and restructuring and power technoloqies,2008.drpt 2008,third international conference on,2008,2525– 2529
[46]吴国旸,肖洋,翁莎莎.风电场短期风速预测探讨.吉林电力,2005,181(6):21-24
[47]谢开贵,周家启.变权组合预测模型研究.系统工程理论与实践,2000,20(7):36-39
[48]韩颖,黄小原,李丽君.多步变权组合预测法及其应用.东北大学学报(自然科学版),2007,28(7):1061-1064
[49]唐小我,曹长修,金德运.组合预测最优加权向量的进一步研究,1994,13(2):48-49.
[50] Andrew.A,Grewal.M. Kalman filtering:Theory and practice using Matlab. IEEE,2008,511-517
[51]飞思科技产品研发中心.神经网络理论与MATLAB 7实现.北京:电子工业出版社,2005,123-189
[52]周丌利,康耀红.神经网络模型及其MATLAB仿真程序设计.北京:清华大学出版社,2005,118-176
[53] Gordon,N.J,Sahnond,D.J and Smith, A.F.M.Novel Approach to Nonlinear/Non.Gaussian Bayesian State Estimation. IEEE Proceedings of Radar and Signal Processing,1993,140(2):107-113
[54] Choy.R,Edelman.A. Parallel matlab:doing it right. proceedings of the IEEE, 2005,93(2):331-341