风电场风速软测量与预测及短期风速数值模拟方法研究
|
摘要
风力发电技术的研究越来越深入,风电场风速问题的研究是其重要的组成部分。本文在近年国内外关于风电场风速研究成果的基础上,针对风力发电机组控制、风电场运行以及风电机组仿真的需要展开研究,主要工作内容和创新成果如下:
1.详细分析了风速特性,针对风力发电机组控制、风电场并网运行和风力发电机组仿真等领域,提炼出风力发电技术中关于风电场风速的三个问题:变速风力发电机组风速软测量,风电场风速预测和短期风速数值模拟。
2.由于风速在空间和时间上的随机变化导致风力机风轮在扫风面上所受风速并不一致,风力机所受实际风速无法直接测量。建立了基于支持向量机的变速风力发电机组风速软测量模型,用于估算风力机所受的实际风速。并提出一种融合风速软测量信号的最大风能追踪控制方案。解决了风速样本在线提取、支持向量机在线训练和风速软测量信号与模糊逻辑控制器融合应用等问题。建立的支持向量机风速软测量模型具有很好的效果;融合风速软测量信息的最大追踪控制方案对于快速变化的风速具有更好的跟踪效果,能够更多地捕获风能。
3.风电场风速预测是减少风电场并网运行给电网带来不良影响的有效手段。为了提高预测精度,应用可靠分类方法对预测误差进行补偿,取得了较好的效果。该方法能够有效的进行误差补偿,进一步提高了预测结果的精度。
4.基于实测数据的实验表明,总体预测精度的提高无法避免单次误差过大。为了避免单次预测误差过大带来的决策风险,提出了一种风电场风速容许区间预测方法。风速容许区间预测不是仅仅给出一个风速的预测值,而是给出一个一定置信水平下的风速值区间。该方法可以根据给定的置信水平得到一个风速容许区间,风速容许区间的半径表征了风速预测结果的可能误差的大小。根据风速容许区间半径的大小采取措施,可以有效的避免某一次预测误差过大带来的决策风险。
5.针对风力发电机组仿真的需要,设计了一种应用小波变换在小波域上进行风速谱模拟的方法。首先,对高斯白噪声进行小波分解;然后,根据风速谱密度对各个频段的小波系数进行收缩处理;最后,利用处理过的小波系数重构信号,得到符合一定功率谱密度特性的风速时间序列。通过和已有算法以及实测风速数据的对比表明,该方法得到的风速时间序列能够更好的逼近风速的谱密度特性,能够方便地用于不同场址的风速模拟,更易于工程应用。
Wind power technology is more and more in depth study.The research of wind speed in wind farm is an important part of wind power genaration's study.Based on results of published papers,the main content of this thesis is as follow:
1.Analysis of wind speed features is done,and three questions about wind speed in wind farm are epurated aimed at wind power generation technology.The three questions are:wind speed soft sonsor for wind turbin controls,wind speed prediction in wind farm and short term wind speed simulation.
2.The effective wind speed for wind turbine can not be measured directly.Soft sensor modeling for effective wind speed was proposed based on support vector regression(SVR),and the effective wind speed was estimated by using the SVR-based model that relates the corresponding variable with other measurements.A max power point track method integrated with wind speed soft sensor is proposed.The problems about wind speed sampling,SVM trainning and the fusion of wind speed infomation and fuzzy logic controller are solved.The proposed method is able to capture more wind energy.
3.The primary issue of wind speed forecasting error compensation is the forecasting error estimation.Forecasting error estimation is converted into a samples classification problem in this paper.Firstly,support vector regression model is trained by wind speed time serials;and the analytics of forecasting errors is done with test samples. Secondly,according to the corresponding margin of error of different samples divided into several classes,to facilitate the training of confidence machine.Finally,confidence machine estimates the margin of forecasting errors.Experimental results show that the accuracy and reliability of the classification can be used to reduce the risk of wrong-compensation,and the proposed approach can achieve higher quality of mean hourly wind speed forecasting.
4.An approach of wind speed tolerance intervals prediction is proposed.In support vector regression prediction basis,wind speed tolerance intervals are predicted using inductive confidence machine.Wind speed tolerance interval's width and confidence reflect the accuracy and reliability of the prediction.Compared to pure wind speed forecasting,the accuracy and reliability of the prediction can be used to reduce the risk of decision-making.
5.Short-term wind speed simulation is an important part of wind turbine generation simulation.A wind speed spectrum fitting approach using wavelet transform is proposed. Firstly,Gaussian white noise is decomposed using wavelet transform.Secondly,the wavelet coefficients of every frequency band are shrunk according to the wind speed's power spectrum.Finally,the wind speed signal is reconstructed using the shrunk wavelet coefficients,and a wind speed time series is proposed.By comparing with other method and real wind speed,it was shown that the proposed approach can approximate the spectral density characteristics of wind speeds more accurate.
1.详细分析了风速特性,针对风力发电机组控制、风电场并网运行和风力发电机组仿真等领域,提炼出风力发电技术中关于风电场风速的三个问题:变速风力发电机组风速软测量,风电场风速预测和短期风速数值模拟。
2.由于风速在空间和时间上的随机变化导致风力机风轮在扫风面上所受风速并不一致,风力机所受实际风速无法直接测量。建立了基于支持向量机的变速风力发电机组风速软测量模型,用于估算风力机所受的实际风速。并提出一种融合风速软测量信号的最大风能追踪控制方案。解决了风速样本在线提取、支持向量机在线训练和风速软测量信号与模糊逻辑控制器融合应用等问题。建立的支持向量机风速软测量模型具有很好的效果;融合风速软测量信息的最大追踪控制方案对于快速变化的风速具有更好的跟踪效果,能够更多地捕获风能。
3.风电场风速预测是减少风电场并网运行给电网带来不良影响的有效手段。为了提高预测精度,应用可靠分类方法对预测误差进行补偿,取得了较好的效果。该方法能够有效的进行误差补偿,进一步提高了预测结果的精度。
4.基于实测数据的实验表明,总体预测精度的提高无法避免单次误差过大。为了避免单次预测误差过大带来的决策风险,提出了一种风电场风速容许区间预测方法。风速容许区间预测不是仅仅给出一个风速的预测值,而是给出一个一定置信水平下的风速值区间。该方法可以根据给定的置信水平得到一个风速容许区间,风速容许区间的半径表征了风速预测结果的可能误差的大小。根据风速容许区间半径的大小采取措施,可以有效的避免某一次预测误差过大带来的决策风险。
5.针对风力发电机组仿真的需要,设计了一种应用小波变换在小波域上进行风速谱模拟的方法。首先,对高斯白噪声进行小波分解;然后,根据风速谱密度对各个频段的小波系数进行收缩处理;最后,利用处理过的小波系数重构信号,得到符合一定功率谱密度特性的风速时间序列。通过和已有算法以及实测风速数据的对比表明,该方法得到的风速时间序列能够更好的逼近风速的谱密度特性,能够方便地用于不同场址的风速模拟,更易于工程应用。
Wind power technology is more and more in depth study.The research of wind speed in wind farm is an important part of wind power genaration's study.Based on results of published papers,the main content of this thesis is as follow:
1.Analysis of wind speed features is done,and three questions about wind speed in wind farm are epurated aimed at wind power generation technology.The three questions are:wind speed soft sonsor for wind turbin controls,wind speed prediction in wind farm and short term wind speed simulation.
2.The effective wind speed for wind turbine can not be measured directly.Soft sensor modeling for effective wind speed was proposed based on support vector regression(SVR),and the effective wind speed was estimated by using the SVR-based model that relates the corresponding variable with other measurements.A max power point track method integrated with wind speed soft sensor is proposed.The problems about wind speed sampling,SVM trainning and the fusion of wind speed infomation and fuzzy logic controller are solved.The proposed method is able to capture more wind energy.
3.The primary issue of wind speed forecasting error compensation is the forecasting error estimation.Forecasting error estimation is converted into a samples classification problem in this paper.Firstly,support vector regression model is trained by wind speed time serials;and the analytics of forecasting errors is done with test samples. Secondly,according to the corresponding margin of error of different samples divided into several classes,to facilitate the training of confidence machine.Finally,confidence machine estimates the margin of forecasting errors.Experimental results show that the accuracy and reliability of the classification can be used to reduce the risk of wrong-compensation,and the proposed approach can achieve higher quality of mean hourly wind speed forecasting.
4.An approach of wind speed tolerance intervals prediction is proposed.In support vector regression prediction basis,wind speed tolerance intervals are predicted using inductive confidence machine.Wind speed tolerance interval's width and confidence reflect the accuracy and reliability of the prediction.Compared to pure wind speed forecasting,the accuracy and reliability of the prediction can be used to reduce the risk of decision-making.
5.Short-term wind speed simulation is an important part of wind turbine generation simulation.A wind speed spectrum fitting approach using wavelet transform is proposed. Firstly,Gaussian white noise is decomposed using wavelet transform.Secondly,the wavelet coefficients of every frequency band are shrunk according to the wind speed's power spectrum.Finally,the wind speed signal is reconstructed using the shrunk wavelet coefficients,and a wind speed time series is proposed.By comparing with other method and real wind speed,it was shown that the proposed approach can approximate the spectral density characteristics of wind speeds more accurate.
引文
[1]王承煦,张源.风力发电.北京:中国电力出版社,2003.
[2]陈甲斌.中国石油供需格局演变及应对措施.国土资源,2006,26(11):26-28.
[3]秦大河,罗勇.全球气候变化的原因和未来变化趋势.科学对社会的影响,2008,8(2):16-21.
[4]时璨丽,李俊峰.我国”十一五”可再生能源发展展望.中国科技成果,2006,16(2):197-201.
[5]李俊峰,时璩丽.世界可再生能源发展的大趋势.中国科技成果,2005,15(13):83-89.
[6]国家发展改革委.能源发展“十一五”规划.北京:国家发展改革委,2007.
[7]国家发展改革委.可再生能源发展“十一五”规划.北京:国家发展改革委,2007.
[8]王庆一.可再生能源现状、前景与政策.中国电力,2002,35(1):68-73.
[9]李俊峰,高虎,施鹏飞等.中国风电发展报告·2007.北京:中国环境科学出版社,2003.
[10]梁志鹏,庄幸.风电特许权--引导风电规模化发展的新机制.中国建设动态,2005,15(10):61-63.
[11]徐大平,张新房,柳亦兵.风力发电控制问题综述.中国电力,2005,38(4):70-74.
[12]张杰.中小尺度天气学.北京:气象出版社,2006.
[13]叶杭冶.风力发电机组的控制技术.北京:机械工业出版社,2006.
[14]贺德馨.风工程与工业空气动力学.北京:国防工业出版社,2006.
[15]何力.最新风电工程技术手册.北京:中国科技文化出版社,2006.
[16]丁明,吴义纯,张立军.风电场风速概率分布参数计算方法的研究.中国电机工程学报,2005,25(10):107-110.
[17]姚兴佳,王士荣,董丽萍.风力发电技术讲座(六)风电场及风力发电机并网运行.可再生能源,2006(6):98-101.
[18]Boser B E,Guyon I M,Vapnik V.A training algorithm for optimal margin classifiers.Fifth Annual Workshop on Computational Learning Theory,ACM.Denver,Colorado USA,1992.
[19]阎辉,张学工,李衍达.支持向量机与最小二乘法的关系研究.清华大学学报.2001,41(9):77-80.
[20]Suykens J A K,Vandewalle J.Least squares support vector machines classifiers Neural Network Letters,1999,19(3):293,300.
[21]Vladimir N.Vapnik.Statistical Learning Theory.Wiley Inter science,USA.
[22]Vapnik V N,张学工[译].统计学习理论的本质.北京:清华大学出版社,2000.
[23]Steven G.Support vector machines classification and regression.ISIS Technical Report.Image Speech & Intelligent Systems Group,University of Southampton.May 10,1998.
[24]Kwok T.J.Support vector mixture for classification and regression problems.Proceedings of Fourteeth International Conference on Pattern Recognition.1998,(1):255-258.
[25]Smola A.J.Sch(o|¨)lkopf B.A tutorial on support vector regression.NeuroCOLT Technical Report NC-TR-98-030,Toyal Holloway College,University of London,UK,1998.Statistics and Computing,2001.
[26]M.A.Mohandes,T.O.Halawani,S.Rehman.Support vector machines for wind speed prediction.Renewable Energy,2004,29:939-947.
[27]许建华,张学工,李衍达.支持向量机的新发展.控制与决策.2004,19(5):481-484,495.
[28]张学工.关于统计学习理论与向量机.自动化学报,2000,26(1):32-42.
[29]Hsu C.W,Lin C.J.A simple decomposition method for support vector machines.Machine Learning,2002,46(1):291-314.
[30]Theodore B.Trafalis,Huseyin Ince.Support vector machine for regression and application to financial forecasting.Proceedings of the international joint conference on neural networks,Neural Computing:new challenges and perspectives for the new millennium,Como,Italy,July 24-27,2000,6:348-353.
[31]Cao L.J.,Tay F.E.H.Support vector machine with adaptive parameters in financial time series forecasting.IEEE transactions on neural networks,2003,14(6):1506-1518.
[32]Haasdonk,B.Feature space interpretation of SVMs with indefinite kernels.IEEE transactions on pattern analysis and machine intelligence,2005,27(4):482-492.
[33]Kuan-Ming Lin,Chih-Jen Lin.A study on reduced support vector machines.IEEE transactions on neural networks,2003,14(6):1449-1459.
[34]Melgani F,Bazi Y.Classification of electrocardiogram signals with support vector machines and particle swarm optimization.IEEE transactions on information technology in biomedicine,2008,12(5):667-677.
[35]Youshen Xia,Jun Wang.A one-layer recurrent neural network for support vector machine learning.IEEE transactions on systems,man,and cybernetics.2004,34(2):1261-1269.
[36]Jayadeva,Khemchandani R,Chandra,S.Twin support vector machines for pattern classification.IEEE transactions on pattern analysis and machine intelligence,2007,29(5):905-910.
[37]ChihChung Chang,ChihJen Lin.LIBSVM:a library for support vector machines,2001.Software available at http://www.csie.ntu.edu.tw/～cjlin/libsvm.
[38]H Papadopoulos,K Proedrou,V Vovk.Inductive Confidence Machines for Regression,Machine Learning:ECML,2002 - Springer.
[39]Gammerman,V Vovk,Hedging prediction in machine learning,the Second Computer Journal Lecture,2007.
[40]Vovk,V.On-line confidence machines are well-calibrated,The 43rd Annual IEEE Symposium on 16-19 Nov.2002:187 - 196.
[41]Alex Gammerman and Vladimir Vovk.Kolmogorov Complexity:Sources,Theory and Applications.The Computer Journal,1999,42(4):252-255.
[42]Ma Xin.Adaptive extremum control and wifld turbine control.Technical University of Denmark,1997.
[43]张新房,徐大平,柳亦兵,等.大型变速风力发电机组的风速软测量.太阳能学报,2006,27(4):321-325.
[44]马勇,黄德先,金以慧.基于支持向量机的软测量建模方法.信息与控制,2004,33(4):417-421.
[45]吴燕玲,卢建刚,孙优贤.基于多目标遗传规划的软测量建模方法.传感技术学报,2007,20(9):2029-2034.
[46]韩璞,王东风.基于神经网络的火电厂烟气含氧量软测量.信息与控制,2001,30(2):189-193.
[47]韩璞,乔弘,王东风,等.火电厂热工参数软测量技术的发展和现状.仪器仪表学报,2007,28(6):1139-1146.
[48]冯瑞,张浩然.基于SVM的软测量建模.信息与控制,2002,31(6):567-571.
[49]刘载文,王正祥,王小艺,等.过程神经元网络学习算法及软测量方法的研究.系统仿真学报,2007,19(7):1456-1459.
[50]颜学峰.基于径基函数-力口权偏最小二乘回归的干点软测量.自动化学报,2007,33(2):193-196.
[51]刘瑞兰,牟盛静,苏宏业,等.基于支持向量机和粒子群算法的软测量建模.控制理论与应用,2006,23(6):895-899,906.
[52]袁平,毛志忠,王福利.基于多支持向量机的软测量模型.系统仿真学报,2006,18(6):1458-1461,1465.
[53]李勇,邵诚.一种新的灰关联分析算法在软测量中的应用.自动化学报,2006,32(2):311-317.
[54]郑小霞,钱锋.基于PCA和最小二乘支持向量机的软测量建模.系统仿真学报,2006,18(3):739-741.
[55]仝卫国,杨耀权,金秀章.基于RBF神经网络的气体流量软测量模型研究.中国电机工程学报,2006,26(1):66-69.
[56]常玉清,王福利,王小刚,等.基于支持向量机的软测量方法及其在生化过程中的应用.仪器仪表学报,2006,27(3):241-244,271.
[57]李亚西 蒋晓春 朱晓光 林资旭 许洪华.一种大功率风电机组变换器新型控制技术研究.太阳能学报,2009,30(1):129-136.
[58]郭家虎 张鲁华 蔡旭.双馈风力发电机的精确线性化解耦控制.电机与控制学报,2009,13(1):57-62.
[59]张先勇 舒杰 吴捷.无刷双馈风力发电机组的自抗扰功率解耦控制.太阳能学报,2008,29(12):1477-1483.
[60]姚兴佳 周洁 邓英 衣传宝 郭庆鼎.风力发电机组的LPV H∞控制器设计.沈阳工业大学学报,2008,30(5):514-519.
[61]曹云峰 蔡旭.变速恒频双馈风力发电系统最大功率跟踪算法的比较与仿真研究.华东电力,2008,36(10):78-82.
[62]邵桂萍 李建林 赵斌 许洪华.兆瓦级变速恒频风电机组变速控制策略的研究.太阳能学报,2008,29(9):1167-1171.
[63]任丽娜 焦晓红 邵立平.风力发电机速度跟踪自适应控制研究.太阳能学报,2008,29(8):1021-1027.
[64]吕跃刚 秦涛 肖运启 徐大平.变速恒频风力发电机组无扰投切控制技术.可再生能源,2008,26(4):13-18.
[65]高峰 徐大平 吕跃刚.大型风力发电机组的前馈模糊-PI变桨距控制.动力工程,2008,28(4):537-542,588.
[66]吴国祥 黄建明 陈国呈 蔚兰 俞俊杰.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.
[67]徐大平 肖运启 吕跃刚 李炜.基于模糊逻辑的双馈型风电机组最优功率控制.太阳能学报,2008,29(6):644-651.
[68]李元诚,方廷健,于尔铿.短期负荷预测的支持向量机方法研究.中国电机工程学报,2003,6,23(6):55-59.
[69]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(5):6-11.
[70]潘迪夫 刘辉 李燕飞.风电场风速短期多步预测改进算法.中国电机工程学报,2008,28(26):87-91.
[71]杜颖 卢继平 李青 邓颖玲.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.
[72]丁明 吴伟 吴红斌 茆美琴 张颖媛.风速概率分布参数预测及应用.电网技术,2008,32(14):10-14.
[73]邵璠 孙育河 梁岚珍.基于时间序列法的风电场风速预测研究.华东电力,2008,36(7):26-29.
[74]韩爽 杨勇平 刘永前.三种方法在风速预测中的应用研究.华北电力大学学报,2008,35(3):57-61.
[75]张彦宁 康龙云 周世琼 曹秉刚.小波分析应用于风力发电预测控制系统中的风速预测.太阳能学报,2008,29(5):520-524.
[76]孙春顺 王耀南 李欣然.小时风速的向量自回归模型及应用.中国电机工程学报,2008,28(14):112-117.
[77]蒋金良 林广明.基于ARIMA模型的自动站风速预测.控制理论与应用,2008,25(2):374-376.
[78]周世琼 康龙云 张彦宁 曹秉刚。基于MATLAB系统辨识工具箱的风信号预测.太阳能学报,2008,29(4):417-421.
[79]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.
[80]A.Sfetsos.A Novel Approach for the Forecasting of Mean Hourly Wind Speed Time Series.Renewable Energy,2002,27(10):163-174.
[81]Mohamed A.Mohandes,Shafiqur Rehrnan.A neural networks approach for wind speed prediction.Renewable Energy,1998,13(3):345-354.
[82]Matevosyan J.,Soder L.Minimization of imbalance cost trading wind power on the short-term power market.IEEE transactions on power systems,2006,21(3):1396-1404.
[83]Fouly T.H.M.,Saadany,E.F.,Salama M.M.A.Grey predictor for wind energy conversion systems output power prediction.IEEE transactions on power systems,2006,21(3):1450-1452.
[84]Karki,R.,Po Hu,Billinton,R.A simplified wind power generation model for reliability evaluation.IEEE transactions on energy conversion,2006,21(2):533-540.
[85]Fouly T.H.M.,Saadany E.F.,Salama M.M.A.Improved Grey predictor rolling models for wind power prediction.Generation,Transmission & Distribution,IET,2007,1(6):928-937.
[86]Watson S.J,Landberg L.,Halliday J.A.Application of wind speed forecasting to the integration of wind energy into a large scale power system,IEE Proceedings Generation,Transmission and Distribution,1994,141(4):357-362.
[87]Soder,L.Reserve margin planning in a wind-hydro-thermal power system.IEEE transactions on power systems,1993,8(2):564-571.
[88]Damousis I.G,Alexiadis M.C,Theocharis,J.B,Dokopoulos P.S.A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation.IEEE transactions on energy conversion,2004,19(2):352-361.
[89]Alexiadis M.C.,Dokopoulos,P.S.,Sahsamanoglou,H.S.Wind speed and power forecasting based on spatial correlation models.IEEE transactions on energy conversion,1999,14(3):836-842.
[90]李东东 陈陈.风力发电机组动态模型研究.中国电机工程学报,2005,25(3):115-119.
[91]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.
[92]孙延奎.小波分析及其应用[M].北京:机械工业出版社,2005.
[93]Ingrid D.小波十讲[M].北京:国防工业出版社,2003.
[94]Mallat S G.A Theory for Multiresolution Signal Decomposition:The Wavelet Representation.IEEE TRANSACTIONSO N PATTERNA NALYSISA ND MACHINE INTELLIGENCE.1989,11(7):674-693.
[95]王耀南,孙春顺,李欣然.用实测风速校正的短期风速仿真研究.中国电机工程学报.2008(11):94-100.
[96]Cohen J E,Newman C M,Cohen A E,et al.Spectral mimicry:a method of synthesizing matching time series with diferent fourier spectra.Circuits system signal process.1999,18(3):431-442.
[97]Welfonder E,Neifer R,Spanner M.Development and experimental identification of dynamic models for wind turbines Control engineering practice.1997,5(1):63-73.
[98]Nichita C,Luca D,Dakyo B,et al.Large Band Simulation of the Wind Speed for Real Time Wind Turbine Simulators.IEEE TRANSACTIONS ON ENERGY CONVERSION.2002,17(4):523-529.
[99]李东东,陈陈.风力发电系统动态仿真的风速模型.中国电机工程学报.2005,25(21):42-44.
[100]Petre Stoica,Randolph Moses.Spectral Analysis of Signals[M].Prentice Hall,USA.2005.
[101]J.G.Slootweg,S.W.H.de Haan,H.Polinder etc,General Model for Representing Variable Speed Wind Turbines In Power System Dynamics Simulations,IEEE Trans.on Power System,2003,18(1):144-151.
[102]李辉,韩力,何蓓.双馈发电机最大风能捕获和转换策略的仿真.系统仿真学报.2007,19(15):3591-3594.
[103]Karki R,Po Hu,Billinton R.A simplified wind power generation model for reliability evaluation.IEEE transactions on energy conversion,2006,21(2):533-540.
[104]Vilar C,Usaola J,Amaris,H.A frequency domain approach to wind turbines for flicker analysis.IEEE power engineering review,2002,22(11):54-54.
[105]Vilar C,Usaola J,Amaris H.A frequency domain approach to wind turbines for flicker analysis.IEEE transactions on energy conversion,2003,18(2):335-341.
[106]Monaldo F.M.,Thompson D.R,Beal R.C,Pichel W.G.Comparison of SAR-derived wind speed with model predictions and ocean buoy measurements.IEEE transactions on geoscience and remote sensing,2001,39(12):2587-2600.
[2]陈甲斌.中国石油供需格局演变及应对措施.国土资源,2006,26(11):26-28.
[3]秦大河,罗勇.全球气候变化的原因和未来变化趋势.科学对社会的影响,2008,8(2):16-21.
[4]时璨丽,李俊峰.我国”十一五”可再生能源发展展望.中国科技成果,2006,16(2):197-201.
[5]李俊峰,时璩丽.世界可再生能源发展的大趋势.中国科技成果,2005,15(13):83-89.
[6]国家发展改革委.能源发展“十一五”规划.北京:国家发展改革委,2007.
[7]国家发展改革委.可再生能源发展“十一五”规划.北京:国家发展改革委,2007.
[8]王庆一.可再生能源现状、前景与政策.中国电力,2002,35(1):68-73.
[9]李俊峰,高虎,施鹏飞等.中国风电发展报告·2007.北京:中国环境科学出版社,2003.
[10]梁志鹏,庄幸.风电特许权--引导风电规模化发展的新机制.中国建设动态,2005,15(10):61-63.
[11]徐大平,张新房,柳亦兵.风力发电控制问题综述.中国电力,2005,38(4):70-74.
[12]张杰.中小尺度天气学.北京:气象出版社,2006.
[13]叶杭冶.风力发电机组的控制技术.北京:机械工业出版社,2006.
[14]贺德馨.风工程与工业空气动力学.北京:国防工业出版社,2006.
[15]何力.最新风电工程技术手册.北京:中国科技文化出版社,2006.
[16]丁明,吴义纯,张立军.风电场风速概率分布参数计算方法的研究.中国电机工程学报,2005,25(10):107-110.
[17]姚兴佳,王士荣,董丽萍.风力发电技术讲座(六)风电场及风力发电机并网运行.可再生能源,2006(6):98-101.
[18]Boser B E,Guyon I M,Vapnik V.A training algorithm for optimal margin classifiers.Fifth Annual Workshop on Computational Learning Theory,ACM.Denver,Colorado USA,1992.
[19]阎辉,张学工,李衍达.支持向量机与最小二乘法的关系研究.清华大学学报.2001,41(9):77-80.
[20]Suykens J A K,Vandewalle J.Least squares support vector machines classifiers Neural Network Letters,1999,19(3):293,300.
[21]Vladimir N.Vapnik.Statistical Learning Theory.Wiley Inter science,USA.
[22]Vapnik V N,张学工[译].统计学习理论的本质.北京:清华大学出版社,2000.
[23]Steven G.Support vector machines classification and regression.ISIS Technical Report.Image Speech & Intelligent Systems Group,University of Southampton.May 10,1998.
[24]Kwok T.J.Support vector mixture for classification and regression problems.Proceedings of Fourteeth International Conference on Pattern Recognition.1998,(1):255-258.
[25]Smola A.J.Sch(o|¨)lkopf B.A tutorial on support vector regression.NeuroCOLT Technical Report NC-TR-98-030,Toyal Holloway College,University of London,UK,1998.Statistics and Computing,2001.
[26]M.A.Mohandes,T.O.Halawani,S.Rehman.Support vector machines for wind speed prediction.Renewable Energy,2004,29:939-947.
[27]许建华,张学工,李衍达.支持向量机的新发展.控制与决策.2004,19(5):481-484,495.
[28]张学工.关于统计学习理论与向量机.自动化学报,2000,26(1):32-42.
[29]Hsu C.W,Lin C.J.A simple decomposition method for support vector machines.Machine Learning,2002,46(1):291-314.
[30]Theodore B.Trafalis,Huseyin Ince.Support vector machine for regression and application to financial forecasting.Proceedings of the international joint conference on neural networks,Neural Computing:new challenges and perspectives for the new millennium,Como,Italy,July 24-27,2000,6:348-353.
[31]Cao L.J.,Tay F.E.H.Support vector machine with adaptive parameters in financial time series forecasting.IEEE transactions on neural networks,2003,14(6):1506-1518.
[32]Haasdonk,B.Feature space interpretation of SVMs with indefinite kernels.IEEE transactions on pattern analysis and machine intelligence,2005,27(4):482-492.
[33]Kuan-Ming Lin,Chih-Jen Lin.A study on reduced support vector machines.IEEE transactions on neural networks,2003,14(6):1449-1459.
[34]Melgani F,Bazi Y.Classification of electrocardiogram signals with support vector machines and particle swarm optimization.IEEE transactions on information technology in biomedicine,2008,12(5):667-677.
[35]Youshen Xia,Jun Wang.A one-layer recurrent neural network for support vector machine learning.IEEE transactions on systems,man,and cybernetics.2004,34(2):1261-1269.
[36]Jayadeva,Khemchandani R,Chandra,S.Twin support vector machines for pattern classification.IEEE transactions on pattern analysis and machine intelligence,2007,29(5):905-910.
[37]ChihChung Chang,ChihJen Lin.LIBSVM:a library for support vector machines,2001.Software available at http://www.csie.ntu.edu.tw/～cjlin/libsvm.
[38]H Papadopoulos,K Proedrou,V Vovk.Inductive Confidence Machines for Regression,Machine Learning:ECML,2002 - Springer.
[39]Gammerman,V Vovk,Hedging prediction in machine learning,the Second Computer Journal Lecture,2007.
[40]Vovk,V.On-line confidence machines are well-calibrated,The 43rd Annual IEEE Symposium on 16-19 Nov.2002:187 - 196.
[41]Alex Gammerman and Vladimir Vovk.Kolmogorov Complexity:Sources,Theory and Applications.The Computer Journal,1999,42(4):252-255.
[42]Ma Xin.Adaptive extremum control and wifld turbine control.Technical University of Denmark,1997.
[43]张新房,徐大平,柳亦兵,等.大型变速风力发电机组的风速软测量.太阳能学报,2006,27(4):321-325.
[44]马勇,黄德先,金以慧.基于支持向量机的软测量建模方法.信息与控制,2004,33(4):417-421.
[45]吴燕玲,卢建刚,孙优贤.基于多目标遗传规划的软测量建模方法.传感技术学报,2007,20(9):2029-2034.
[46]韩璞,王东风.基于神经网络的火电厂烟气含氧量软测量.信息与控制,2001,30(2):189-193.
[47]韩璞,乔弘,王东风,等.火电厂热工参数软测量技术的发展和现状.仪器仪表学报,2007,28(6):1139-1146.
[48]冯瑞,张浩然.基于SVM的软测量建模.信息与控制,2002,31(6):567-571.
[49]刘载文,王正祥,王小艺,等.过程神经元网络学习算法及软测量方法的研究.系统仿真学报,2007,19(7):1456-1459.
[50]颜学峰.基于径基函数-力口权偏最小二乘回归的干点软测量.自动化学报,2007,33(2):193-196.
[51]刘瑞兰,牟盛静,苏宏业,等.基于支持向量机和粒子群算法的软测量建模.控制理论与应用,2006,23(6):895-899,906.
[52]袁平,毛志忠,王福利.基于多支持向量机的软测量模型.系统仿真学报,2006,18(6):1458-1461,1465.
[53]李勇,邵诚.一种新的灰关联分析算法在软测量中的应用.自动化学报,2006,32(2):311-317.
[54]郑小霞,钱锋.基于PCA和最小二乘支持向量机的软测量建模.系统仿真学报,2006,18(3):739-741.
[55]仝卫国,杨耀权,金秀章.基于RBF神经网络的气体流量软测量模型研究.中国电机工程学报,2006,26(1):66-69.
[56]常玉清,王福利,王小刚,等.基于支持向量机的软测量方法及其在生化过程中的应用.仪器仪表学报,2006,27(3):241-244,271.
[57]李亚西 蒋晓春 朱晓光 林资旭 许洪华.一种大功率风电机组变换器新型控制技术研究.太阳能学报,2009,30(1):129-136.
[58]郭家虎 张鲁华 蔡旭.双馈风力发电机的精确线性化解耦控制.电机与控制学报,2009,13(1):57-62.
[59]张先勇 舒杰 吴捷.无刷双馈风力发电机组的自抗扰功率解耦控制.太阳能学报,2008,29(12):1477-1483.
[60]姚兴佳 周洁 邓英 衣传宝 郭庆鼎.风力发电机组的LPV H∞控制器设计.沈阳工业大学学报,2008,30(5):514-519.
[61]曹云峰 蔡旭.变速恒频双馈风力发电系统最大功率跟踪算法的比较与仿真研究.华东电力,2008,36(10):78-82.
[62]邵桂萍 李建林 赵斌 许洪华.兆瓦级变速恒频风电机组变速控制策略的研究.太阳能学报,2008,29(9):1167-1171.
[63]任丽娜 焦晓红 邵立平.风力发电机速度跟踪自适应控制研究.太阳能学报,2008,29(8):1021-1027.
[64]吕跃刚 秦涛 肖运启 徐大平.变速恒频风力发电机组无扰投切控制技术.可再生能源,2008,26(4):13-18.
[65]高峰 徐大平 吕跃刚.大型风力发电机组的前馈模糊-PI变桨距控制.动力工程,2008,28(4):537-542,588.
[66]吴国祥 黄建明 陈国呈 蔚兰 俞俊杰.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.
[67]徐大平 肖运启 吕跃刚 李炜.基于模糊逻辑的双馈型风电机组最优功率控制.太阳能学报,2008,29(6):644-651.
[68]李元诚,方廷健,于尔铿.短期负荷预测的支持向量机方法研究.中国电机工程学报,2003,6,23(6):55-59.
[69]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(5):6-11.
[70]潘迪夫 刘辉 李燕飞.风电场风速短期多步预测改进算法.中国电机工程学报,2008,28(26):87-91.
[71]杜颖 卢继平 李青 邓颖玲.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.
[72]丁明 吴伟 吴红斌 茆美琴 张颖媛.风速概率分布参数预测及应用.电网技术,2008,32(14):10-14.
[73]邵璠 孙育河 梁岚珍.基于时间序列法的风电场风速预测研究.华东电力,2008,36(7):26-29.
[74]韩爽 杨勇平 刘永前.三种方法在风速预测中的应用研究.华北电力大学学报,2008,35(3):57-61.
[75]张彦宁 康龙云 周世琼 曹秉刚.小波分析应用于风力发电预测控制系统中的风速预测.太阳能学报,2008,29(5):520-524.
[76]孙春顺 王耀南 李欣然.小时风速的向量自回归模型及应用.中国电机工程学报,2008,28(14):112-117.
[77]蒋金良 林广明.基于ARIMA模型的自动站风速预测.控制理论与应用,2008,25(2):374-376.
[78]周世琼 康龙云 张彦宁 曹秉刚。基于MATLAB系统辨识工具箱的风信号预测.太阳能学报,2008,29(4):417-421.
[79]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.
[80]A.Sfetsos.A Novel Approach for the Forecasting of Mean Hourly Wind Speed Time Series.Renewable Energy,2002,27(10):163-174.
[81]Mohamed A.Mohandes,Shafiqur Rehrnan.A neural networks approach for wind speed prediction.Renewable Energy,1998,13(3):345-354.
[82]Matevosyan J.,Soder L.Minimization of imbalance cost trading wind power on the short-term power market.IEEE transactions on power systems,2006,21(3):1396-1404.
[83]Fouly T.H.M.,Saadany,E.F.,Salama M.M.A.Grey predictor for wind energy conversion systems output power prediction.IEEE transactions on power systems,2006,21(3):1450-1452.
[84]Karki,R.,Po Hu,Billinton,R.A simplified wind power generation model for reliability evaluation.IEEE transactions on energy conversion,2006,21(2):533-540.
[85]Fouly T.H.M.,Saadany E.F.,Salama M.M.A.Improved Grey predictor rolling models for wind power prediction.Generation,Transmission & Distribution,IET,2007,1(6):928-937.
[86]Watson S.J,Landberg L.,Halliday J.A.Application of wind speed forecasting to the integration of wind energy into a large scale power system,IEE Proceedings Generation,Transmission and Distribution,1994,141(4):357-362.
[87]Soder,L.Reserve margin planning in a wind-hydro-thermal power system.IEEE transactions on power systems,1993,8(2):564-571.
[88]Damousis I.G,Alexiadis M.C,Theocharis,J.B,Dokopoulos P.S.A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation.IEEE transactions on energy conversion,2004,19(2):352-361.
[89]Alexiadis M.C.,Dokopoulos,P.S.,Sahsamanoglou,H.S.Wind speed and power forecasting based on spatial correlation models.IEEE transactions on energy conversion,1999,14(3):836-842.
[90]李东东 陈陈.风力发电机组动态模型研究.中国电机工程学报,2005,25(3):115-119.
[91]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.
[92]孙延奎.小波分析及其应用[M].北京:机械工业出版社,2005.
[93]Ingrid D.小波十讲[M].北京:国防工业出版社,2003.
[94]Mallat S G.A Theory for Multiresolution Signal Decomposition:The Wavelet Representation.IEEE TRANSACTIONSO N PATTERNA NALYSISA ND MACHINE INTELLIGENCE.1989,11(7):674-693.
[95]王耀南,孙春顺,李欣然.用实测风速校正的短期风速仿真研究.中国电机工程学报.2008(11):94-100.
[96]Cohen J E,Newman C M,Cohen A E,et al.Spectral mimicry:a method of synthesizing matching time series with diferent fourier spectra.Circuits system signal process.1999,18(3):431-442.
[97]Welfonder E,Neifer R,Spanner M.Development and experimental identification of dynamic models for wind turbines Control engineering practice.1997,5(1):63-73.
[98]Nichita C,Luca D,Dakyo B,et al.Large Band Simulation of the Wind Speed for Real Time Wind Turbine Simulators.IEEE TRANSACTIONS ON ENERGY CONVERSION.2002,17(4):523-529.
[99]李东东,陈陈.风力发电系统动态仿真的风速模型.中国电机工程学报.2005,25(21):42-44.
[100]Petre Stoica,Randolph Moses.Spectral Analysis of Signals[M].Prentice Hall,USA.2005.
[101]J.G.Slootweg,S.W.H.de Haan,H.Polinder etc,General Model for Representing Variable Speed Wind Turbines In Power System Dynamics Simulations,IEEE Trans.on Power System,2003,18(1):144-151.
[102]李辉,韩力,何蓓.双馈发电机最大风能捕获和转换策略的仿真.系统仿真学报.2007,19(15):3591-3594.
[103]Karki R,Po Hu,Billinton R.A simplified wind power generation model for reliability evaluation.IEEE transactions on energy conversion,2006,21(2):533-540.
[104]Vilar C,Usaola J,Amaris,H.A frequency domain approach to wind turbines for flicker analysis.IEEE power engineering review,2002,22(11):54-54.
[105]Vilar C,Usaola J,Amaris H.A frequency domain approach to wind turbines for flicker analysis.IEEE transactions on energy conversion,2003,18(2):335-341.
[106]Monaldo F.M.,Thompson D.R,Beal R.C,Pichel W.G.Comparison of SAR-derived wind speed with model predictions and ocean buoy measurements.IEEE transactions on geoscience and remote sensing,2001,39(12):2587-2600.