风电场风电功率预测方法研究
|
摘要
近年,随着风力发电技术的发展,由于大型并网型风电场调度的需要,风电场风电功率预测逐渐受到人们的广泛重视。风力发电受气象条件的影响,其输出功率具有不确定性。而在电力系统中,供电和需求之间必须时刻保持平衡。过对风电功率进行准确的预测,可以降低电网旋转备用容量,减小风力发电系统成本,并且为电网运行调度提供可靠的依据,保障电网可靠、安全和经济运行。风电功率预测方法的研究不断深入的同时,数值天气预报也逐渐成为当前主流的风电功率预测系统的重要输入之一。
论文通过对黑龙江哈尔滨依兰华富风电场风电功率时间序列的分析,研究了风电场尾流效应对风电功率预测方法的影响,提出了结合风电功率时间序列的混沌属性、基于相空间重构的神经网络分析方法,应用该方法对风电功率时间序列建立了BP神经网络和GRNN神经网络预测模型,同时通过GRNN神经网络探讨了数值天气预报应用于实际风电功率预测的途径。此外,论文采用ARMA、噪声场合下的ARMA、ARMA与BP神经网络集成等多种时间序列分析方法对风电功率时间序列建立了不同的预测模型。最后,论文将多种预测模型结合数值天气预报数据通过LabVIEW和SQL server数据库两种应用软件应用到实际风电场运行高度中。
论文对尾流效应和季节性因素进行理论分析后,充分利用依兰风电场的历史数据,通过相邻风机实际测量的风向和风速数据,验证了风电场的尾流效应；通过风速和功率数据的对比分析,从风速的随机性中发现其季节性规律。
论文对风力发电输出功率系统的内在特性进行研究,通过计算时间延迟、嵌入维数以及最大Lyapunov指数等重要参数,检验了风电功率时间序列的混沌属性。提出了基于相空间重构的BP神经网络模型对风电功率时间序列进行预测的方法。通过对不同季节的风电功率时间序列参数的计算,验证了风力发电系统的季节性,充分说明了结合季节性因素进行风电功率预测的必要性。
论文对人工神经网络分析方法进行研究后,重点探讨了BP神经网络和GRNN神经网络等理论在风电功率预测中的应用方法,并利用它们分别建立了预测网络模型。针对不同时间段的风电功率时间序列建立了不同的BP神经网络预测模型,通过对比预测精度验证了风电功率时间序列的季节性因素。
论文对时间序列分析方法进行研究后,重点应用样本偏自相关函数(PACF)定阶、AIC值最小定阶、BIC值最小定阶三种定阶准则确定AR模型参数后,利用长自回归模型法对风电功率时间序列建立了不同的ARMA模型。之后,考虑到系统测量噪声的影响,应用HOYW定阶方法建立了噪声场合下的ARMA模型。
为了克服现有方法在处理非平稳信号的不足,论文将风电功率时间序列分解为确定项和随机项两个部分,确定项用BP人工神经网络拟合,表示时序的非平稳趋向；随机项表示平衡的随机成分,用ARMA模型拟合。通过样本数据分析,建立了ARMA与BP神经网络集成预测模型。
论文还详细研究了数值天气预报数据在风电功率中预测中的应用,建立结合数值天气预报的风电功率中期预测模型。针对风电场当地不确定因素对数值天气预报准确度的干扰影响,通过GRNN神经网络分析方法分析将数值天气预报数据与历史测量的气象数据之间的对应关系应用于风电功率预测,得到最佳风速预测值,进而通过拟合风速功率曲线得出最佳功率预测数据。
论文针对实际运行的风电场,采用多种预测模型组合建模的方法,按照国家能源局下达的风电场风电功率预测规范,开发了以LabVIEW为主要研发环境,以SQL server数据库为存储工具,集用户身份辨识、数据采集、预测模型建立分析及存储、数据处理、风电场设置、用户管理等功能于一体的集成化风电功率预测系统,并成功应用于其它几家大型并网风电场,取得了较好的预测效果。
Along with the development of wind power technology. wind power forecasting gradually gets the extensive attention from people. Wind power generation is influenced by weather conditions and the output power is uncertain. But it is required to keep a balance between generation and demand in a power system. An accurate wind power forecasting will decrease the reserve capacity, reduce the cost of the system and provide reference to the operation and dispatch of the power grid. With the deepening of the research for the methods of wind power forecasting, numerical weather forecasting is also gradually becomes the mainstream of the wind power forecasting system.
In this context, some theories are researched with the time series of wind power generating capacity from Yilan. such as Chaos characteristic, phase space reconstruction. artificial neural network (ANN) method and time series analysis method. Some forecasting models, for example Back-propagation (BP). General Regression Neural Network (GRNN). ARMA. ARMA model on noise occasions, are structured. And the way how to use NWP data in the forecasting is discussed through GRNN neural network. Then, an integrated wind power forecasting System is established by LabVIEW and SQL language.
After the study on the wake effect and seasonal factors, wind wake effect and the seasonal characteristic are verified by the historical data of Yilan wind farm.
Research is done on the inherent characteristic of wind power time series. The largest Lyapunov exponent is bigger than zero, which proves that the wind power generation system has chaos characteristics. The accuracy from different training data which are colleted from different seasons, also proves that the wind power generation system has seasonal characteristic.
Different forecasting models are structured on the basis of the research of BP neural network based on phase space reconstruction and GRNN method. Seasonal characteristic of the wind power generation system is tested and verified by the different forecast accuracy from the models, which are structured from training data.
Three different methods of how to determine the order of ARMA are taken into account and different ARMA models are built. The measurement noise is considered and an ARMA model on noise occasions based on HOYW method is built.
Considering the instability characteristic, the wind power time series is divided into determine item and random item two parts, and ARMA and BP neural network method integrated forecasting model is structured.
Uncertainty factors from wind farms usually lower the accuracy of NWP. so when the NWP data is used in wind power forecasting, analysis of the relationship between NWP data and Actual meteorological data is very necessary. Here, artificial neural network method is well deserved.
According to the wind farm wind power forecasting standard from National Energy Bureau, Synthesized so multiple forecasting model modeling methods, LabVIEW and SQL language are combined to establish integrated wind power forecasting System which includes identification function, data acquisition function, analysis and forecasting model function, data processing function and son on. And this forecasting System has been successfully used in large grid wind farms.
论文通过对黑龙江哈尔滨依兰华富风电场风电功率时间序列的分析,研究了风电场尾流效应对风电功率预测方法的影响,提出了结合风电功率时间序列的混沌属性、基于相空间重构的神经网络分析方法,应用该方法对风电功率时间序列建立了BP神经网络和GRNN神经网络预测模型,同时通过GRNN神经网络探讨了数值天气预报应用于实际风电功率预测的途径。此外,论文采用ARMA、噪声场合下的ARMA、ARMA与BP神经网络集成等多种时间序列分析方法对风电功率时间序列建立了不同的预测模型。最后,论文将多种预测模型结合数值天气预报数据通过LabVIEW和SQL server数据库两种应用软件应用到实际风电场运行高度中。
论文对尾流效应和季节性因素进行理论分析后,充分利用依兰风电场的历史数据,通过相邻风机实际测量的风向和风速数据,验证了风电场的尾流效应；通过风速和功率数据的对比分析,从风速的随机性中发现其季节性规律。
论文对风力发电输出功率系统的内在特性进行研究,通过计算时间延迟、嵌入维数以及最大Lyapunov指数等重要参数,检验了风电功率时间序列的混沌属性。提出了基于相空间重构的BP神经网络模型对风电功率时间序列进行预测的方法。通过对不同季节的风电功率时间序列参数的计算,验证了风力发电系统的季节性,充分说明了结合季节性因素进行风电功率预测的必要性。
论文对人工神经网络分析方法进行研究后,重点探讨了BP神经网络和GRNN神经网络等理论在风电功率预测中的应用方法,并利用它们分别建立了预测网络模型。针对不同时间段的风电功率时间序列建立了不同的BP神经网络预测模型,通过对比预测精度验证了风电功率时间序列的季节性因素。
论文对时间序列分析方法进行研究后,重点应用样本偏自相关函数(PACF)定阶、AIC值最小定阶、BIC值最小定阶三种定阶准则确定AR模型参数后,利用长自回归模型法对风电功率时间序列建立了不同的ARMA模型。之后,考虑到系统测量噪声的影响,应用HOYW定阶方法建立了噪声场合下的ARMA模型。
为了克服现有方法在处理非平稳信号的不足,论文将风电功率时间序列分解为确定项和随机项两个部分,确定项用BP人工神经网络拟合,表示时序的非平稳趋向；随机项表示平衡的随机成分,用ARMA模型拟合。通过样本数据分析,建立了ARMA与BP神经网络集成预测模型。
论文还详细研究了数值天气预报数据在风电功率中预测中的应用,建立结合数值天气预报的风电功率中期预测模型。针对风电场当地不确定因素对数值天气预报准确度的干扰影响,通过GRNN神经网络分析方法分析将数值天气预报数据与历史测量的气象数据之间的对应关系应用于风电功率预测,得到最佳风速预测值,进而通过拟合风速功率曲线得出最佳功率预测数据。
论文针对实际运行的风电场,采用多种预测模型组合建模的方法,按照国家能源局下达的风电场风电功率预测规范,开发了以LabVIEW为主要研发环境,以SQL server数据库为存储工具,集用户身份辨识、数据采集、预测模型建立分析及存储、数据处理、风电场设置、用户管理等功能于一体的集成化风电功率预测系统,并成功应用于其它几家大型并网风电场,取得了较好的预测效果。
Along with the development of wind power technology. wind power forecasting gradually gets the extensive attention from people. Wind power generation is influenced by weather conditions and the output power is uncertain. But it is required to keep a balance between generation and demand in a power system. An accurate wind power forecasting will decrease the reserve capacity, reduce the cost of the system and provide reference to the operation and dispatch of the power grid. With the deepening of the research for the methods of wind power forecasting, numerical weather forecasting is also gradually becomes the mainstream of the wind power forecasting system.
In this context, some theories are researched with the time series of wind power generating capacity from Yilan. such as Chaos characteristic, phase space reconstruction. artificial neural network (ANN) method and time series analysis method. Some forecasting models, for example Back-propagation (BP). General Regression Neural Network (GRNN). ARMA. ARMA model on noise occasions, are structured. And the way how to use NWP data in the forecasting is discussed through GRNN neural network. Then, an integrated wind power forecasting System is established by LabVIEW and SQL language.
After the study on the wake effect and seasonal factors, wind wake effect and the seasonal characteristic are verified by the historical data of Yilan wind farm.
Research is done on the inherent characteristic of wind power time series. The largest Lyapunov exponent is bigger than zero, which proves that the wind power generation system has chaos characteristics. The accuracy from different training data which are colleted from different seasons, also proves that the wind power generation system has seasonal characteristic.
Different forecasting models are structured on the basis of the research of BP neural network based on phase space reconstruction and GRNN method. Seasonal characteristic of the wind power generation system is tested and verified by the different forecast accuracy from the models, which are structured from training data.
Three different methods of how to determine the order of ARMA are taken into account and different ARMA models are built. The measurement noise is considered and an ARMA model on noise occasions based on HOYW method is built.
Considering the instability characteristic, the wind power time series is divided into determine item and random item two parts, and ARMA and BP neural network method integrated forecasting model is structured.
Uncertainty factors from wind farms usually lower the accuracy of NWP. so when the NWP data is used in wind power forecasting, analysis of the relationship between NWP data and Actual meteorological data is very necessary. Here, artificial neural network method is well deserved.
According to the wind farm wind power forecasting standard from National Energy Bureau, Synthesized so multiple forecasting model modeling methods, LabVIEW and SQL language are combined to establish integrated wind power forecasting System which includes identification function, data acquisition function, analysis and forecasting model function, data processing function and son on. And this forecasting System has been successfully used in large grid wind farms.
引文
[1]A Burden beyond Bearing. [Online]. Available:http://www.nature.com/news/2009'090429/full/ 458109la.htmll;
[2]Global Wind 2008 Report. [Online]. Available:www.windpowerworks.net;
[3]20% Wind Energy by 2030 Increasing Wind Energy's Contribution to U.S. Electricity Supply. [Online]. Available:http://www.nrel.gov/docs/fy08osti/41869.pdf.
[4]时璟丽.风电市场缘何方兴艾[J].能源,2009,4.
[5]李俊峰,高虎,王仲颖等.2008中国风电发展报告,中国环境科学出版社,北京,2008.
[6]中国可再生能源学会风能专业委员会,2009年中国风电装机容量统计.产业,2010,1：28-33.
[7]Jonathan Collllins, Jeremy Parkes, Andrew Tindal. Forecasting for utility-scale wind farms-the power model challenge [R]. UK:Garrad Hassan & Partners Ltd.
[8]Kurt Rohrig, Bernhard Lange. Application of wind power forecasting tools for power system operations. IEEE Power Engineering Society General Meeting[C],2006.
[9]Billinton R, Hua Chen, Ghajar R. A sequential simulation technique for adequacy evaluation of generating systems including wind energy [J]. IEEE Transactions on Energy Conversion, 1996.11(4): 728-734.
[10]Pinson P, Kariniotakis G N. Wind power forecasting using fuzzy neural networks enhanced with on-line forecasting risk assessment. IEEE Proceedings of Power Technology Conference [C]. Bologna. 2003.2:8.
[11]Damousis I G, Alexiadis M C. Theocharis J B. et al. A fuzzy model for wind speed forecasting and power generation in wind parks using spatial correlation [J]. IEEE Transaction on Energy Conversion, 2004.19(2):352-361.
[12]Damousis I G, Dokopoulos P. A fuzzy expert system for the forecasting of wind speed and Power generation in wind farms [J]. Power Industry Computer Applications,2001,4:63-69.
[13]Wang P, Billinton R. Time-sequential simulation technique for rural distribution system reliability cost/worth evaluation including wind generation as alternative supply[J]. IEEE Proceedings of Generation, Transmission and Distribution,2001,148(4):355-360.
[14]韩爽.风电场功率短期预测方法研究[D].华北电力大学,2008.
[15]Bech J, Hansen A D, Janosi L, et al. Wind farm modeling for power quality [J]. IEEE Industrial Electronics Society,2001,3(29):1959-964.
[16]Castro R M G, Ferreira L A F M. A comparison between chronological and probabilistic methods to estimate wind power capacity credit [J]. Power Systems, IEEE Transactions on,2001,16(4):904-909.
[17]Karki R, Billinton R. Cost-effective wind energy utilization for reliable power supply [J]. IEEE Transactions on Energy Conversion,2004,2(19):435-440.
[18]雷亚洲,王伟胜,印永华,等.风电对电力系统运行的价值分析[J].电网技术,2002,5(26)10-14.
[19]Troen I, Petersen E L. European wind atlas[M]. Roskilde:RisoeNational Laboratory,1989.
[20]Nielsen T S, Madsen H. WPPT-a tool for wind power forecasting[C]. EWEA Special Topic Conference, Kassel,2000.
[21]Giebel G, Lars L, Joensen A K, et al. The zephyr-project-the next generation forecasting system[C]. Procedings of Wind Power for the 21st Century, Kassel, Germany,2000.
[22]Focken U M, Lange H P W. Previento-a wind power forecasting system with an innovative upscaling algorithm[C]. European Wind Energy Conference. Copenhagen. Denmark.2001.
[23]Lange M U. Focken D. Heinemann previento-regional wind power forecasting with risk eontrol[C]. Proceedings of the World Wind Energy Conference. Berlin,2002.
|24] Bernhard L. Kurt R. Bern hard E. et al. wind power forecasting in Germany-recent advances and future challenges[C]. European Wind Energy Conference. Athens.2006.
[25]Bailey B. Brower M C. Zack J. Short-term wind forecasting[C]. European Wind Energy Conference. Nice France.1999.
[26]Zack J W, Brower M C, Bailey B H. Validating of the forewind model in wind forecasting application[C]. EUWEC special topic conference wind power for the 21st century. Kassel. Germany 2000.
[27]Sanchezl O. Ravelo J, Usaola C. et al. Sipreolico-a wind power forecasting system based on flexible combination of dynamic models.application to the spanish power system[C]. First IEA Joint Action Symposium on Wind Forecasting Techniques, Norrkoping, Sweden,2002.
[28]Hatziargyriou N. Contaxis G, Matos M, et al.More care-advice for secure operation of isolated power systems with increased renewable energy penetration and storage[C]. European Wind Energy Conference, Copenhagen.2001.
[29]Bessa. R.J. Miranda, V. Gama. J. Entropy and Correntropy Against Minimum Square Error in Offline and Online Three-Day Ahead Wind Power Forecasting [J]. IEEE Transactions on Power Systems.2009. 4(24):1657-1666.
[30]Lydia. M. Kumar. S.S. A comprehensive overview on wind power forecasting[J].IPEC.2010 Conference Proceedings,2010:268-273.
[31]Vargas. L. Paredes. G. Bustos, G. Data mining techniques for very short term forecasting of wind powe[J]. Bulk Power System Dynamics and Control (iREP)-VIII (iREP).2010 iREP Symposium. 2010:1-7.
[32]Methaprayoon, K. Yingvivatanapong, C. Wei-Jen Lee Liao. J.R. An Integration of ANN Wind Power Estimation Into Unit Commitment Considering the Forecasting Uncertainty[J]. IEEE Transactions on Industry Applications,2007,6(43):1441-1448.
[33]Bludszuweit, H. Dominguez-Navarro, J.A. Llombart, A. Statistical Analysis of Wind Power Forecast Error [J]. IEEE Transactions on Power Systems,2008,3(23)Page(s):983-991.
[34]Shuang Han, Yongqian Liu. The Study of Wind Power Combination forecasting[J]. Power and Energy Engineering Conference (APPEEC),2010 Asia-Pacific,2010:1-4.
[35]Wang Xiaolan, Li Hui. One-Month Ahead forecasting of Wind Speed and Output Power Based on EMD and LSSVM[J]. International Conference on Energy and Environment Technology,2009. ICEET '09.,2009,3:439-442.
[36]Chen Ye, Gengyin Li, Ming Zhou.A combined forecasting method of wind farm power [C].2010 5th International Conference on Critical Infrastructure (CRIS),2010:1-5.
[37]Hui Zhou, Mei Huang, Xinfliua Wu. Forecast of Wind speed and power of wind generator based on pattern recognition; International Conference on Industrial Mechatronics and Automation[C].2009. ICIMA 2009:504-508.
[38]王丽婕,廖晓钟,高爽等.并网型大型风电场风力发电功率时间序列的混沌属性分析[J].北京理工大学学报,2007,12(27)：1077-1080.
[39]冬雷,王丽婕,高爽,等.基于混沌时间序列的大型风电场发电功率预测建模与研究[J].电工技术学报,2008,12：125-129.
[40]Dong Lei, Wang Lijie, Liao Xiaozhong, forecasting of Wind Power Generation Based on Time Series Wavelet Transform for Large Wind Farm[C]. PESA09. HongKong2009.
[41]郝颖,冬雷,等.自回归滑动平均模型的风力发电功率预测方法研究[J].电气传动.增刊,2009.
[42]王丽婕,冬雷,胡国飞.等.基于多嵌入维数的风力发电机功率组合预测模型[J].控制与决策,2010,4(25)：577-580.
[43]Lijie Wang. Annelies Gerber. Jun Liang, Lei Dong. Xiaozhong Liao. Wind power forecasting for reduction of system reserve[C]. UPEC2010. Cardiff. United Kingdom. Aug 31-Sep 3.2010.
[44]Lijie Wang. Lei Dong, Ying Hao. Xiaozhong Liao, wind power forecasting Using Wavelet Transform and Chaotic Characteristic[C]. The 1st World Non-Grid-Connected Wind Power and Energy Conference 2009, Nanjing China.
[45]王丽婕,廖晓钟,高阳,等.风电场发电功率的的建模和预测研究综述[J].电力系统保护与控制,2009.7：118-121.
[46]王丽婕,冬雷,廖晓钟,高阳.基于小波分析的风电场短期发电功率预测[J].中国电机工程学报,2009,10(29)：30-33.
[47]常太华,王璐,马巍.基于AR, ARIMA模型的风速预测[J].华东电力,2010,1(38)：59-62.
[48]井天军,阮睿,杨明皓.基于等效平均风速的风力发电功率预测[J].电力系统自动化,2009,33(24)：83-87.
[49]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].力系统保护与控制.2010,38(19)：151-159.
[50]管胜利.基于局域波分解及时间序列的风电场风速预测研究[J].华北电力技术,2009,1：10-13.
[51]吴兴华,周晖,黄梅.基于模式识别的风电场风速和发电功率预测[J].继电器,2008,36(1)：27-32.
[52]曾杰.张华.基于最小二乘支持向量机的风速预测摸型[J].电网技术,2009 33(18)：144-147.
[53]付伟,杨先一,冯伟等.遗传神经网络在风电场风速估计中的应用[J].现代科学仪器.2009.12(6)：48-52.
[54]肖永山.王维庆,霍晓萍.基于神经网络的风电场风速时间序列预测研究[J].节能技术.2007,25(2)：106-108.
[55]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.
[56]张国强,张伯明.丛于组合预测的风电场风速及风电机功率预测[J].电力系统自动化,2009,(18).
[57]戚双斌,王维庆,张新燕.基于支持向量机的风速与风功率预测方法研究[J].华东电力,2009,(09).
[58]黄金花,彭晖.风电场短期风电功率的神经网络方法预测研究[J].电工电气,2009,9.
[59]刘纯,范高锋,王伟胜,等.风电场输出功率的组合预测模型[J].电网技术,2009,13.
[60]冬雷,高爽,廖晓钟,等.风力发电系统发电容量时间序列的混沌属性分析[J].太阳能学报,2007,11(28)：1290-1294.
[61]Bernhard Ernst, Brett Oakleaf, Mark L.Ahlstrom, et al. Predicting the Wind [J]. IEEE Power & Energy Magazine,2007,11:79-89.
[62]M. Negnevitsky, P. Johnson, S. Santoso. Short term wind power forecasting using hybrid intelligent systems [C]. IEEE Power Engineering Society General Meeting,24-28 June 2007:1-4.
[63]郝颖.风电场风力发电功率预测方法研究[D].北京：北京理工大学,2010.
[64]El-Fouly T H M, El-Saadany E F, Salama M M A.Improved Grey Predictor Rolling Models for wind power forecasting[J]. IEEE Proceedings on Gener, Transm & Distrib,2007,1(6):928-937.
[65]周毅,侯志明,刘宇迪.数值天气预报丛础[M].北京：气象出版社,2003.
[66]Nielsen. T.S.. Madsen. H.. Nielsen, et al. Short-term wind power forecasting using advanced statistical methods. In:European Wind Energy Conference & Exhibition 2006 (2006).
[67]Li S. Wind Power forecasting Using Recurrent Multilayer Perceptron Neural Networks[A]. in: Power Engineering Society General Meeting, IEEE[C].2003.2325-2330.
[68]CAO Lei, LI Ran. Short-Term Wind Speed Forecasting Model for Wind Farm Based on Wavelet Decomposition[J]. IEEE Trans on DRPT,2008:2525-2529.
[69]Sideratos G, Hatziargyriou N. Using Radial Basis Neural Networks to Estimate Wind Power Production[A]. in:Power Engineering Society General Meeting,IEEE[C].2007,1-7.
[70]Cameron Potter, Martin Ringrose. Short-Term Wind Forcasting Techniques for Power Generation[A]. AUPEC.2004.
[71]Torben Skov Nielsen, Alfred Joensen. A New Reference for Wind Power Forcasting[A]. Wind Energy.1998,1:29-34.
[72]庄晓丹.大型风电场的风能损失计算[J].电力系统自动化,2009,33(12)：85-90.
[73]陈树勇,戴慧珠,白晓民,等.尾流效应对风电场输出功率的影响[J].中国电力,1998,31(11)：28-31.
[74]郑睿敏.李建华,李作红,等.考虑尾流效应的风电场建模以及随机潮流计算[J].西安交通大学学报.2008.42(12)：1515-1520.
[75]Dvid A.Spera. Wind Turbine Technology Fundamental Concepts of Wind Turbine Engineer-ing[D]. New York:ASME,1994:313-315.
[76]蒋小亮.风电并网对电力系统可靠性和备用影响研究[D].上海：上海交通大学,2011.
[77]吕金虎,陆君安,陈上华.混沌时间序列分析及其应用[M].武汉：武汉大学出版社,2002.
[78]Wolf A. Swift J B, Swinney H L. Vastano J A. Determining Lyapunov exponents from a time series[J]. Physica D,1985,16:285-317.
[79]孙克辉,国强,盛利元,等.Lyapunov指数计算算法的设计与实现[J].计算机工程与应用,2004,35：12-14.
[80]Rosenstein M T, Collins J J, Deluca C J. A practical method for calculating largest Lyapunov exponents from small data sets[J]. Physica D,1993,65:117-187.
[81]王丽婕.风力发电功率的建模与预测研究[D].北京：北京理工大学,2011.
[82]路应金,唐小我,张勇.供应链产品定价行为混沌特性及其混沌预测[J].控制与决策,2006,21(4)：445-448.
[83]M.B.Kennel, R.Brown, H.D.I.Abarbanel. Determining embedding dimension for phase space reconstruction using a geometrical construction[J]. Physical Review A,1992,45(6):3403-3411.
[84]Cao Liangyue. Practical method for determining the minimum embedding dimension of a scalar time series[J]. Physica D,1997, (110):43-50.
[85]Haykin S, Xiao Bo Li. Detection of signals in chaos [J]. Proceedings of the IEEE,1995,83(1):95-122.
[86]Cherkassky V, Mulier F. Learning from Data:Concepts, Theory and Methods[M]. NY:John Viley & Sons,1997.
[87]魏海坤.神经网络结构设计的理论与方法[M].国防工业出版社,2005.
[88]袁曾任.人工神经元网络及其应用[M].北京：清华大学出版社,1999.
[89]飞思科技产品研发中心.神经网络理论与matlab7实现[M].电子工业出版社,2005.
[90]周开利,康耀红.神经网络模型及其matlab仿真程序设计[M].清华大学出版社,2005.
[91]韩力群.人工神经网络理论、设计及应用[M].北京：化学工业出版社,2002.
[92]高阳,朴在林,张铁岩,马仕海2010. Study of ultra-short term wind power forecasting method, 2010 International Conference on Future Industrial Engineering and Application.193-197.
[93]冯志鹏,宋希庚,薛冬新.基于广义回归神经网络的时间序列预测研究[J].振动、测试与诊断,2003,23(2)：104-109.
[94]郭晶,孙伟娟.神经网络理论与MATLAB7实现[M].北京：电子工业出版社,2007.
[95]蔡凯.风电场对电力系统稳定性的影响[D].镇汀：江苏大学.2008.
[96]孟勇.风电功率预测系统的研究与开发[D].天津：天津大学,2010.
[97]杨志凌.风电场功率短期预测方法优化的研究[D].北京：华北电大大学,2011.
[98]王宇,胡赞,曹仲义.Origin软件在化工原理实验数据拟合中的应用[J].计算机技术应用.2010,1(27:):86-93.
[99]武新,张永胜.Origin在曲线拟合中的应用[J].计算机工程与应用,2005,17：206-208.
[100]杨叔子.吴雅,轩建平.等.时间序列分析的工程应用[M].武汉：华中科技大学出版社,2007,6：22-280.
[101]王振龙.时间序列分析[M].北京：中国统计出版社,2006.
[102]潘红宇.时间序列分析[M].北京：对外经济贸易大学出版社,2006,1：39-122.
[103]吴怀宇.时间序列分析与综合[M].武汉：武汉大学出版社,2004.
[104]邵璠,孙育河,梁岚珍.丛于ARMA模型的风电场风速短期预测[J].电网与清洁能源,2008.24(1).
[105]蒋金良,林广明.基于ARIMA模型的自动站风速预测[J].控制理论与应用,2008,25(2).
[106]王红军,田铮,韩四儿.混合自回归滑动平均模型----MARMA[J].系统工程理论与实践,2006,11.
[107]常学将,陈敏,王明生.时间序列分析[M].北京：高等教育出版社,1993,11：119-196.
[108]高阳,朴在林，张旭鹏,等.基于噪声场合下ARMA模型的风力发电量预测[J].电力系统控制与保护.2010,38(20)：164-167.
[109]王秀峰,卢桂章.系统建模与辨识[M].北京：电子工业出版社,2004.
[1]0]刘晓,曾徉虎,刘存宇.边坡非线性位移的神经网络-时间序列分析[J].岩石力学与工程学报,2005,24(19)：3499-3504.
[111]卢建昌,王柳.基于时序分析的神经网络短期负荷预测模型研究[J].中国电力,2005,38(7)：11-14.
[112]周求湛,钱志鸿,刘萍萍,等.虚拟仪器与LabVIEW7 Express程序设计[M].北京：北京航空航天大学出版社,2004,6：1-165.
[113]赵子汀,王丹.数据库原理与SQL Server应用[M].北京：机械工业出版社,2006,9：1-86.
[2]Global Wind 2008 Report. [Online]. Available:www.windpowerworks.net;
[3]20% Wind Energy by 2030 Increasing Wind Energy's Contribution to U.S. Electricity Supply. [Online]. Available:http://www.nrel.gov/docs/fy08osti/41869.pdf.
[4]时璟丽.风电市场缘何方兴艾[J].能源,2009,4.
[5]李俊峰,高虎,王仲颖等.2008中国风电发展报告,中国环境科学出版社,北京,2008.
[6]中国可再生能源学会风能专业委员会,2009年中国风电装机容量统计.产业,2010,1：28-33.
[7]Jonathan Collllins, Jeremy Parkes, Andrew Tindal. Forecasting for utility-scale wind farms-the power model challenge [R]. UK:Garrad Hassan & Partners Ltd.
[8]Kurt Rohrig, Bernhard Lange. Application of wind power forecasting tools for power system operations. IEEE Power Engineering Society General Meeting[C],2006.
[9]Billinton R, Hua Chen, Ghajar R. A sequential simulation technique for adequacy evaluation of generating systems including wind energy [J]. IEEE Transactions on Energy Conversion, 1996.11(4): 728-734.
[10]Pinson P, Kariniotakis G N. Wind power forecasting using fuzzy neural networks enhanced with on-line forecasting risk assessment. IEEE Proceedings of Power Technology Conference [C]. Bologna. 2003.2:8.
[11]Damousis I G, Alexiadis M C. Theocharis J B. et al. A fuzzy model for wind speed forecasting and power generation in wind parks using spatial correlation [J]. IEEE Transaction on Energy Conversion, 2004.19(2):352-361.
[12]Damousis I G, Dokopoulos P. A fuzzy expert system for the forecasting of wind speed and Power generation in wind farms [J]. Power Industry Computer Applications,2001,4:63-69.
[13]Wang P, Billinton R. Time-sequential simulation technique for rural distribution system reliability cost/worth evaluation including wind generation as alternative supply[J]. IEEE Proceedings of Generation, Transmission and Distribution,2001,148(4):355-360.
[14]韩爽.风电场功率短期预测方法研究[D].华北电力大学,2008.
[15]Bech J, Hansen A D, Janosi L, et al. Wind farm modeling for power quality [J]. IEEE Industrial Electronics Society,2001,3(29):1959-964.
[16]Castro R M G, Ferreira L A F M. A comparison between chronological and probabilistic methods to estimate wind power capacity credit [J]. Power Systems, IEEE Transactions on,2001,16(4):904-909.
[17]Karki R, Billinton R. Cost-effective wind energy utilization for reliable power supply [J]. IEEE Transactions on Energy Conversion,2004,2(19):435-440.
[18]雷亚洲,王伟胜,印永华,等.风电对电力系统运行的价值分析[J].电网技术,2002,5(26)10-14.
[19]Troen I, Petersen E L. European wind atlas[M]. Roskilde:RisoeNational Laboratory,1989.
[20]Nielsen T S, Madsen H. WPPT-a tool for wind power forecasting[C]. EWEA Special Topic Conference, Kassel,2000.
[21]Giebel G, Lars L, Joensen A K, et al. The zephyr-project-the next generation forecasting system[C]. Procedings of Wind Power for the 21st Century, Kassel, Germany,2000.
[22]Focken U M, Lange H P W. Previento-a wind power forecasting system with an innovative upscaling algorithm[C]. European Wind Energy Conference. Copenhagen. Denmark.2001.
[23]Lange M U. Focken D. Heinemann previento-regional wind power forecasting with risk eontrol[C]. Proceedings of the World Wind Energy Conference. Berlin,2002.
|24] Bernhard L. Kurt R. Bern hard E. et al. wind power forecasting in Germany-recent advances and future challenges[C]. European Wind Energy Conference. Athens.2006.
[25]Bailey B. Brower M C. Zack J. Short-term wind forecasting[C]. European Wind Energy Conference. Nice France.1999.
[26]Zack J W, Brower M C, Bailey B H. Validating of the forewind model in wind forecasting application[C]. EUWEC special topic conference wind power for the 21st century. Kassel. Germany 2000.
[27]Sanchezl O. Ravelo J, Usaola C. et al. Sipreolico-a wind power forecasting system based on flexible combination of dynamic models.application to the spanish power system[C]. First IEA Joint Action Symposium on Wind Forecasting Techniques, Norrkoping, Sweden,2002.
[28]Hatziargyriou N. Contaxis G, Matos M, et al.More care-advice for secure operation of isolated power systems with increased renewable energy penetration and storage[C]. European Wind Energy Conference, Copenhagen.2001.
[29]Bessa. R.J. Miranda, V. Gama. J. Entropy and Correntropy Against Minimum Square Error in Offline and Online Three-Day Ahead Wind Power Forecasting [J]. IEEE Transactions on Power Systems.2009. 4(24):1657-1666.
[30]Lydia. M. Kumar. S.S. A comprehensive overview on wind power forecasting[J].IPEC.2010 Conference Proceedings,2010:268-273.
[31]Vargas. L. Paredes. G. Bustos, G. Data mining techniques for very short term forecasting of wind powe[J]. Bulk Power System Dynamics and Control (iREP)-VIII (iREP).2010 iREP Symposium. 2010:1-7.
[32]Methaprayoon, K. Yingvivatanapong, C. Wei-Jen Lee Liao. J.R. An Integration of ANN Wind Power Estimation Into Unit Commitment Considering the Forecasting Uncertainty[J]. IEEE Transactions on Industry Applications,2007,6(43):1441-1448.
[33]Bludszuweit, H. Dominguez-Navarro, J.A. Llombart, A. Statistical Analysis of Wind Power Forecast Error [J]. IEEE Transactions on Power Systems,2008,3(23)Page(s):983-991.
[34]Shuang Han, Yongqian Liu. The Study of Wind Power Combination forecasting[J]. Power and Energy Engineering Conference (APPEEC),2010 Asia-Pacific,2010:1-4.
[35]Wang Xiaolan, Li Hui. One-Month Ahead forecasting of Wind Speed and Output Power Based on EMD and LSSVM[J]. International Conference on Energy and Environment Technology,2009. ICEET '09.,2009,3:439-442.
[36]Chen Ye, Gengyin Li, Ming Zhou.A combined forecasting method of wind farm power [C].2010 5th International Conference on Critical Infrastructure (CRIS),2010:1-5.
[37]Hui Zhou, Mei Huang, Xinfliua Wu. Forecast of Wind speed and power of wind generator based on pattern recognition; International Conference on Industrial Mechatronics and Automation[C].2009. ICIMA 2009:504-508.
[38]王丽婕,廖晓钟,高爽等.并网型大型风电场风力发电功率时间序列的混沌属性分析[J].北京理工大学学报,2007,12(27)：1077-1080.
[39]冬雷,王丽婕,高爽,等.基于混沌时间序列的大型风电场发电功率预测建模与研究[J].电工技术学报,2008,12：125-129.
[40]Dong Lei, Wang Lijie, Liao Xiaozhong, forecasting of Wind Power Generation Based on Time Series Wavelet Transform for Large Wind Farm[C]. PESA09. HongKong2009.
[41]郝颖,冬雷,等.自回归滑动平均模型的风力发电功率预测方法研究[J].电气传动.增刊,2009.
[42]王丽婕,冬雷,胡国飞.等.基于多嵌入维数的风力发电机功率组合预测模型[J].控制与决策,2010,4(25)：577-580.
[43]Lijie Wang. Annelies Gerber. Jun Liang, Lei Dong. Xiaozhong Liao. Wind power forecasting for reduction of system reserve[C]. UPEC2010. Cardiff. United Kingdom. Aug 31-Sep 3.2010.
[44]Lijie Wang. Lei Dong, Ying Hao. Xiaozhong Liao, wind power forecasting Using Wavelet Transform and Chaotic Characteristic[C]. The 1st World Non-Grid-Connected Wind Power and Energy Conference 2009, Nanjing China.
[45]王丽婕,廖晓钟,高阳,等.风电场发电功率的的建模和预测研究综述[J].电力系统保护与控制,2009.7：118-121.
[46]王丽婕,冬雷,廖晓钟,高阳.基于小波分析的风电场短期发电功率预测[J].中国电机工程学报,2009,10(29)：30-33.
[47]常太华,王璐,马巍.基于AR, ARIMA模型的风速预测[J].华东电力,2010,1(38)：59-62.
[48]井天军,阮睿,杨明皓.基于等效平均风速的风力发电功率预测[J].电力系统自动化,2009,33(24)：83-87.
[49]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].力系统保护与控制.2010,38(19)：151-159.
[50]管胜利.基于局域波分解及时间序列的风电场风速预测研究[J].华北电力技术,2009,1：10-13.
[51]吴兴华,周晖,黄梅.基于模式识别的风电场风速和发电功率预测[J].继电器,2008,36(1)：27-32.
[52]曾杰.张华.基于最小二乘支持向量机的风速预测摸型[J].电网技术,2009 33(18)：144-147.
[53]付伟,杨先一,冯伟等.遗传神经网络在风电场风速估计中的应用[J].现代科学仪器.2009.12(6)：48-52.
[54]肖永山.王维庆,霍晓萍.基于神经网络的风电场风速时间序列预测研究[J].节能技术.2007,25(2)：106-108.
[55]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.
[56]张国强,张伯明.丛于组合预测的风电场风速及风电机功率预测[J].电力系统自动化,2009,(18).
[57]戚双斌,王维庆,张新燕.基于支持向量机的风速与风功率预测方法研究[J].华东电力,2009,(09).
[58]黄金花,彭晖.风电场短期风电功率的神经网络方法预测研究[J].电工电气,2009,9.
[59]刘纯,范高锋,王伟胜,等.风电场输出功率的组合预测模型[J].电网技术,2009,13.
[60]冬雷,高爽,廖晓钟,等.风力发电系统发电容量时间序列的混沌属性分析[J].太阳能学报,2007,11(28)：1290-1294.
[61]Bernhard Ernst, Brett Oakleaf, Mark L.Ahlstrom, et al. Predicting the Wind [J]. IEEE Power & Energy Magazine,2007,11:79-89.
[62]M. Negnevitsky, P. Johnson, S. Santoso. Short term wind power forecasting using hybrid intelligent systems [C]. IEEE Power Engineering Society General Meeting,24-28 June 2007:1-4.
[63]郝颖.风电场风力发电功率预测方法研究[D].北京：北京理工大学,2010.
[64]El-Fouly T H M, El-Saadany E F, Salama M M A.Improved Grey Predictor Rolling Models for wind power forecasting[J]. IEEE Proceedings on Gener, Transm & Distrib,2007,1(6):928-937.
[65]周毅,侯志明,刘宇迪.数值天气预报丛础[M].北京：气象出版社,2003.
[66]Nielsen. T.S.. Madsen. H.. Nielsen, et al. Short-term wind power forecasting using advanced statistical methods. In:European Wind Energy Conference & Exhibition 2006 (2006).
[67]Li S. Wind Power forecasting Using Recurrent Multilayer Perceptron Neural Networks[A]. in: Power Engineering Society General Meeting, IEEE[C].2003.2325-2330.
[68]CAO Lei, LI Ran. Short-Term Wind Speed Forecasting Model for Wind Farm Based on Wavelet Decomposition[J]. IEEE Trans on DRPT,2008:2525-2529.
[69]Sideratos G, Hatziargyriou N. Using Radial Basis Neural Networks to Estimate Wind Power Production[A]. in:Power Engineering Society General Meeting,IEEE[C].2007,1-7.
[70]Cameron Potter, Martin Ringrose. Short-Term Wind Forcasting Techniques for Power Generation[A]. AUPEC.2004.
[71]Torben Skov Nielsen, Alfred Joensen. A New Reference for Wind Power Forcasting[A]. Wind Energy.1998,1:29-34.
[72]庄晓丹.大型风电场的风能损失计算[J].电力系统自动化,2009,33(12)：85-90.
[73]陈树勇,戴慧珠,白晓民,等.尾流效应对风电场输出功率的影响[J].中国电力,1998,31(11)：28-31.
[74]郑睿敏.李建华,李作红,等.考虑尾流效应的风电场建模以及随机潮流计算[J].西安交通大学学报.2008.42(12)：1515-1520.
[75]Dvid A.Spera. Wind Turbine Technology Fundamental Concepts of Wind Turbine Engineer-ing[D]. New York:ASME,1994:313-315.
[76]蒋小亮.风电并网对电力系统可靠性和备用影响研究[D].上海：上海交通大学,2011.
[77]吕金虎,陆君安,陈上华.混沌时间序列分析及其应用[M].武汉：武汉大学出版社,2002.
[78]Wolf A. Swift J B, Swinney H L. Vastano J A. Determining Lyapunov exponents from a time series[J]. Physica D,1985,16:285-317.
[79]孙克辉,国强,盛利元,等.Lyapunov指数计算算法的设计与实现[J].计算机工程与应用,2004,35：12-14.
[80]Rosenstein M T, Collins J J, Deluca C J. A practical method for calculating largest Lyapunov exponents from small data sets[J]. Physica D,1993,65:117-187.
[81]王丽婕.风力发电功率的建模与预测研究[D].北京：北京理工大学,2011.
[82]路应金,唐小我,张勇.供应链产品定价行为混沌特性及其混沌预测[J].控制与决策,2006,21(4)：445-448.
[83]M.B.Kennel, R.Brown, H.D.I.Abarbanel. Determining embedding dimension for phase space reconstruction using a geometrical construction[J]. Physical Review A,1992,45(6):3403-3411.
[84]Cao Liangyue. Practical method for determining the minimum embedding dimension of a scalar time series[J]. Physica D,1997, (110):43-50.
[85]Haykin S, Xiao Bo Li. Detection of signals in chaos [J]. Proceedings of the IEEE,1995,83(1):95-122.
[86]Cherkassky V, Mulier F. Learning from Data:Concepts, Theory and Methods[M]. NY:John Viley & Sons,1997.
[87]魏海坤.神经网络结构设计的理论与方法[M].国防工业出版社,2005.
[88]袁曾任.人工神经元网络及其应用[M].北京：清华大学出版社,1999.
[89]飞思科技产品研发中心.神经网络理论与matlab7实现[M].电子工业出版社,2005.
[90]周开利,康耀红.神经网络模型及其matlab仿真程序设计[M].清华大学出版社,2005.
[91]韩力群.人工神经网络理论、设计及应用[M].北京：化学工业出版社,2002.
[92]高阳,朴在林,张铁岩,马仕海2010. Study of ultra-short term wind power forecasting method, 2010 International Conference on Future Industrial Engineering and Application.193-197.
[93]冯志鹏,宋希庚,薛冬新.基于广义回归神经网络的时间序列预测研究[J].振动、测试与诊断,2003,23(2)：104-109.
[94]郭晶,孙伟娟.神经网络理论与MATLAB7实现[M].北京：电子工业出版社,2007.
[95]蔡凯.风电场对电力系统稳定性的影响[D].镇汀：江苏大学.2008.
[96]孟勇.风电功率预测系统的研究与开发[D].天津：天津大学,2010.
[97]杨志凌.风电场功率短期预测方法优化的研究[D].北京：华北电大大学,2011.
[98]王宇,胡赞,曹仲义.Origin软件在化工原理实验数据拟合中的应用[J].计算机技术应用.2010,1(27:):86-93.
[99]武新,张永胜.Origin在曲线拟合中的应用[J].计算机工程与应用,2005,17：206-208.
[100]杨叔子.吴雅,轩建平.等.时间序列分析的工程应用[M].武汉：华中科技大学出版社,2007,6：22-280.
[101]王振龙.时间序列分析[M].北京：中国统计出版社,2006.
[102]潘红宇.时间序列分析[M].北京：对外经济贸易大学出版社,2006,1：39-122.
[103]吴怀宇.时间序列分析与综合[M].武汉：武汉大学出版社,2004.
[104]邵璠,孙育河,梁岚珍.丛于ARMA模型的风电场风速短期预测[J].电网与清洁能源,2008.24(1).
[105]蒋金良,林广明.基于ARIMA模型的自动站风速预测[J].控制理论与应用,2008,25(2).
[106]王红军,田铮,韩四儿.混合自回归滑动平均模型----MARMA[J].系统工程理论与实践,2006,11.
[107]常学将,陈敏,王明生.时间序列分析[M].北京：高等教育出版社,1993,11：119-196.
[108]高阳,朴在林，张旭鹏,等.基于噪声场合下ARMA模型的风力发电量预测[J].电力系统控制与保护.2010,38(20)：164-167.
[109]王秀峰,卢桂章.系统建模与辨识[M].北京：电子工业出版社,2004.
[1]0]刘晓,曾徉虎,刘存宇.边坡非线性位移的神经网络-时间序列分析[J].岩石力学与工程学报,2005,24(19)：3499-3504.
[111]卢建昌,王柳.基于时序分析的神经网络短期负荷预测模型研究[J].中国电力,2005,38(7)：11-14.
[112]周求湛,钱志鸿,刘萍萍,等.虚拟仪器与LabVIEW7 Express程序设计[M].北京：北京航空航天大学出版社,2004,6：1-165.
[113]赵子汀,王丹.数据库原理与SQL Server应用[M].北京：机械工业出版社,2006,9：1-86.