河西地区风速变化特征及风能预测方法研究
|
摘要
风能作为一种可再生的清洁能源,可以改善化石能源枯竭的现状,具有调整能源结构和减轻环境污染的双重功效,将是21世纪最有发展前景的绿色能源。但风电受风速变化特性的影响,具有很大的随机性、间歇性、不可控性和反调峰特性,由此给电网调度和电力供应管理构成显著压力。
为实现电力供应系统稳定运行,接入风电必须考虑留有足够的备用电源和调峰容量,这在很大程度上增加了电力系统的运行成本,而且大规模风电的引入已给电力系统平稳运行带来诸多隐患。因此随着风电装机容量的增加,风电穿透功率超过一定值后,为使电网平稳运行,建立有效可靠的风电功率预测系统必将成为电力系统不可或缺的重要组成部分。
风速预测是风电功率和风电场发电量预测的最重要基础和前提,但目前风电场风速预测精度依然不足,风速预测精度的提高成为目前亟待解决的问题。
本文系统分析了甘肃省风能蕴藏量最大的河西地区近60年来风能变化特征,划分了风速变化的特征类型,将风速分型引入风速预测中,提出了一种风速预测的新方法--小波分解自回归结合风速变化分型的预测方法(WDAR2),由此为发展出适合该地区的风速预测系统模型,为提高风速预测及风能评估精度打下基础,为风能稳定调配上网提供技术支持。
(1)每日4次风观测资料的分析结果可以较好代表同期每日24次风资料的分析结果；河西地区在2000年以前风速整体呈现下降趋势,2000年以后风速则呈现增长的趋势；采用Mann-Kendall(M-K)检验法分析河西地区风速的气候变化适用性较差；2005-2010年间瓜州(原名：安西)地区最大风速为18.4m/s,未超过25.Om/s。
(2)可将河西地区风速变化分型为：平缓波动型风速、普通波动型风速(包括尖峰型风速和宽峰型风速)、递增型风速和递减型风速。
(3)在同一区域内,基于气象站地面风观测(10m高度)、区域测风塔(70m高度)风观测资料计算的风能资源量与利用风电场测点(70m高度)风资料的计算结果存在着差异。表明利用气象站或区域测风塔等替代资料计算风电场的风能资源时,需要进行风的相关性分析,做必要的订正。同时,需要将气象站的地面风订正至风机高度。
(4)本文构建的小波分解自回归结合风速变化分型的预测方法(WDAR2)能够用于风速短期预测,并且在一定程度上提高了风速的预测精度。
(5)在用于电力系统调节的短期风速预测方法中,小波分解自回归结合风速变化分型的风速预测方法(WDAR2)优于小波分解自回归的风速预测方法(WDAR1),也优于目前使用较为广泛的自回归风速预测方法(AR)。
As a kind of renewable and clean energy, wind power can make up for improving the present situation of fossil energy exhaustion, and has double efficacy of adjusting the energy structure and reducing environmental pollution, it will be the most promising green energy in21st century. While influenced by wind speed change characteristics, wind power has a lot of randomness, intermittent, uncontrollability and reverse load characteristics, which constitutes a significant pressure to power grid scheduling and power supply management.
For achieving stable operation of wind power supply system, there must be adequate spare power source and load capacity, which will increase the power system operation cost largely, and the introduction of large-scale wind power has already brought a lot of hidden trouble to power system. With the increase of the installed capacity of wind power and wind power penetration power exceeds a certain value, in order to make the power grid operate smoothly, the effective and reliable wind power prediction system will become an important part of electric power system.
Wind speed forecasting is the most important foundation and the premise of wind power and wind farm generated energy prediction, now wind speed forecasting model accuracy in China is still at unsatisfactory level, the wind speed prediction accuracy has become the existing problems to be solved presently.
This paper has analyzed the wind change general rule of He'xi area in the past60years systematically, whose wind energy reserves is biggest in the Gansu province, divided wind speed change characteristics types, and put forward a new method of wind speed forecasting-wavelet decomposition autoregressive combined with wind speed change parting forecast method (WDAR2) by bring wind speed change characteristics types into wind speed prediction, which is suitable for the region, and made the foundation to improve the wind speed prediction and wind power evaluation accuracy, provided technical support for stable wind power deployment accessing to power grid.
1. It is inconspicuous for the analysis of wind speed variation characteristics and results of the impact to change wind observation method; Before2000, the wind speed overall declines in He'xi area, while it is on the trend of growth after2000; The suitability is poor by using Mann- Kendall (M-K) test method to analyse wind climate change in He'xi area. In the years2005-2010, the maximum wind speed of Guazhou (formerly:An'xi) county area is18.4m/s, not more than25.0m/s.
2. The wind speed change in He'xi area can be divided into four types:gentle wave type, common wave type (including peak type and broad peak type), increasing type and decreasing type.
3. There are differences between wind power resources calculated from the observation data based on meteorological station above the ground10m level, wind tower above the ground70m level, and wind power farm above the ground70m level in the same area. It is needed to make wind correlation analysis and do the necessary corrections when using alternatives material such as the observation data of meteorological station or regional wind tower to calculate wind power resources of wind farms. At the same time, it is needed to correct the ground wind of meteorological station to fan height.
4. The wavelet decomposition autoregressive combined with wind speed change parting forecast method (WDAR2) can be used to forecast wind speed in short-term, and the wind speed prediction accuracy is improved in a certain extent.
5. During the short-term wind speed prediction methods that meet the power system control requirements, wavelet decomposition autoregressive combined with wind speed change points type of wind speed forecasting method (WDAR2) is better than wavelet decomposition autoregressive wind speed forecasting method (WDAR1), which is also superior to the current more widely applicable autoregressive wind speed forecasting method (AR).
为实现电力供应系统稳定运行,接入风电必须考虑留有足够的备用电源和调峰容量,这在很大程度上增加了电力系统的运行成本,而且大规模风电的引入已给电力系统平稳运行带来诸多隐患。因此随着风电装机容量的增加,风电穿透功率超过一定值后,为使电网平稳运行,建立有效可靠的风电功率预测系统必将成为电力系统不可或缺的重要组成部分。
风速预测是风电功率和风电场发电量预测的最重要基础和前提,但目前风电场风速预测精度依然不足,风速预测精度的提高成为目前亟待解决的问题。
本文系统分析了甘肃省风能蕴藏量最大的河西地区近60年来风能变化特征,划分了风速变化的特征类型,将风速分型引入风速预测中,提出了一种风速预测的新方法--小波分解自回归结合风速变化分型的预测方法(WDAR2),由此为发展出适合该地区的风速预测系统模型,为提高风速预测及风能评估精度打下基础,为风能稳定调配上网提供技术支持。
(1)每日4次风观测资料的分析结果可以较好代表同期每日24次风资料的分析结果；河西地区在2000年以前风速整体呈现下降趋势,2000年以后风速则呈现增长的趋势；采用Mann-Kendall(M-K)检验法分析河西地区风速的气候变化适用性较差；2005-2010年间瓜州(原名：安西)地区最大风速为18.4m/s,未超过25.Om/s。
(2)可将河西地区风速变化分型为：平缓波动型风速、普通波动型风速(包括尖峰型风速和宽峰型风速)、递增型风速和递减型风速。
(3)在同一区域内,基于气象站地面风观测(10m高度)、区域测风塔(70m高度)风观测资料计算的风能资源量与利用风电场测点(70m高度)风资料的计算结果存在着差异。表明利用气象站或区域测风塔等替代资料计算风电场的风能资源时,需要进行风的相关性分析,做必要的订正。同时,需要将气象站的地面风订正至风机高度。
(4)本文构建的小波分解自回归结合风速变化分型的预测方法(WDAR2)能够用于风速短期预测,并且在一定程度上提高了风速的预测精度。
(5)在用于电力系统调节的短期风速预测方法中,小波分解自回归结合风速变化分型的风速预测方法(WDAR2)优于小波分解自回归的风速预测方法(WDAR1),也优于目前使用较为广泛的自回归风速预测方法(AR)。
As a kind of renewable and clean energy, wind power can make up for improving the present situation of fossil energy exhaustion, and has double efficacy of adjusting the energy structure and reducing environmental pollution, it will be the most promising green energy in21st century. While influenced by wind speed change characteristics, wind power has a lot of randomness, intermittent, uncontrollability and reverse load characteristics, which constitutes a significant pressure to power grid scheduling and power supply management.
For achieving stable operation of wind power supply system, there must be adequate spare power source and load capacity, which will increase the power system operation cost largely, and the introduction of large-scale wind power has already brought a lot of hidden trouble to power system. With the increase of the installed capacity of wind power and wind power penetration power exceeds a certain value, in order to make the power grid operate smoothly, the effective and reliable wind power prediction system will become an important part of electric power system.
Wind speed forecasting is the most important foundation and the premise of wind power and wind farm generated energy prediction, now wind speed forecasting model accuracy in China is still at unsatisfactory level, the wind speed prediction accuracy has become the existing problems to be solved presently.
This paper has analyzed the wind change general rule of He'xi area in the past60years systematically, whose wind energy reserves is biggest in the Gansu province, divided wind speed change characteristics types, and put forward a new method of wind speed forecasting-wavelet decomposition autoregressive combined with wind speed change parting forecast method (WDAR2) by bring wind speed change characteristics types into wind speed prediction, which is suitable for the region, and made the foundation to improve the wind speed prediction and wind power evaluation accuracy, provided technical support for stable wind power deployment accessing to power grid.
1. It is inconspicuous for the analysis of wind speed variation characteristics and results of the impact to change wind observation method; Before2000, the wind speed overall declines in He'xi area, while it is on the trend of growth after2000; The suitability is poor by using Mann- Kendall (M-K) test method to analyse wind climate change in He'xi area. In the years2005-2010, the maximum wind speed of Guazhou (formerly:An'xi) county area is18.4m/s, not more than25.0m/s.
2. The wind speed change in He'xi area can be divided into four types:gentle wave type, common wave type (including peak type and broad peak type), increasing type and decreasing type.
3. There are differences between wind power resources calculated from the observation data based on meteorological station above the ground10m level, wind tower above the ground70m level, and wind power farm above the ground70m level in the same area. It is needed to make wind correlation analysis and do the necessary corrections when using alternatives material such as the observation data of meteorological station or regional wind tower to calculate wind power resources of wind farms. At the same time, it is needed to correct the ground wind of meteorological station to fan height.
4. The wavelet decomposition autoregressive combined with wind speed change parting forecast method (WDAR2) can be used to forecast wind speed in short-term, and the wind speed prediction accuracy is improved in a certain extent.
5. During the short-term wind speed prediction methods that meet the power system control requirements, wavelet decomposition autoregressive combined with wind speed change points type of wind speed forecasting method (WDAR2) is better than wavelet decomposition autoregressive wind speed forecasting method (WDAR1), which is also superior to the current more widely applicable autoregressive wind speed forecasting method (AR).
引文
[1]Furkan Dincer. The analysis on wind energy electricity generation status[J]. Potential and Policies in the World. Renewble and Sustainable Energy Reviews.2011,15(9):5135-5142.
[2]Nikhil Chaudhry, Larry Hughes. Forecasting the Reliability of Wind-energy Systems:A New Approach Using the RL Technique[J]. Applied Energy.2012,96(August):422-430.
[3]Saidur, R., Islam, M.R., Rahim, N.A., Solangi, K.H. A Review on Global Wind Energy Policy[J]. Renewable and Sustainable Energy Reviews.2010,14(7):1744-1762.
[4]Piotr Michalak, Jacek Zimny. Wind Energy Development in The World, Europe and Poland From 1995 to 2009:Current Status and Future Perspectives[J]. Renewable and Sustainable Energy Reviews.2011,15(5):2330-2341.
[5]朱成章.关于中国风能资源储量的质疑[J].中外能源.2010,15(4)：34-39.
[6]杨振斌.中国风能资源[J].大自然.2007(2)：48-49.
[7]中国可再生能源学会风能专业委员会.2011年中国风电装机容量统计.北京：2012.
[8]McVicar, T.R., Van Niel, T.G., Li L.T.,et al. Wind Speed Climatology and Trends for Australia,1975-2006:Capturing the Stilling Phenomenon and Comparison with Near-surface Reanalysis Output[J]. Geophysical Research Letters.2008,35(20):L20403.
[9]Sahin Ahmet Duran. Progress and Recent Trends in Wind Energy[J]. Progress in Energy and Combusion Science.2004,30(5):501-543.
[10]Sesto Ezio, Casale Claudio. Exploitation of Wind As an Energy Source to Meet the World's Electricity Demand[J]. Journal of Wind Energy and Industrial Aerodynamics.1998, 74-76(April):375-387.
[11]李振朝,韦志刚,高荣.近50年河西绿洲地面风的时空变化特征[J].高原气象.2004,23(2)：259-263.
[12]刘苏峡,邱建秀,莫兴国.华北平原1951年至2006年风速变化特征分析[J].资源科学.2009,31(9)：1486-1492.
[13]Mahrt Larry. Surface Wind Direction Variability [J]. Journal of Applied Meteorlogy and Climatology.2010,50(1):144-152.
[14]Kose Ramazan, Arif Ozgur M., Erbas Oguzhan, Tugcu Abtullah. The Analysis of Wind Data and Wind Energy Potential in Kutahya,Turkey[J]. Renewable and Sustainable Energy Reviews.2004,8(3):277-288.
[15]张德,朱蓉,罗勇,俞卫,王澄海.风能模拟系统WEST在中国风能数值模拟中的应用[J].高原气象.2008,27(1)：202-207.
[16]朱飙,李春华,陆登荣.甘肃酒泉区域风能资源评估[J].干旱气象.2009,27(2)：152-156.
[17]田德,王海宽,韩巧丽.浓缩风能型风力发电机的研究与进展[J].农业工程学报,2003,19(Z1)：177-181.
[18]刘细平,林鹤云.风力发电机及风力发电控制技术综述[J].大电机技术.2007(3)：17-20.
[19]程启明,程尹曼,王映斐,汪明媚.风力发电系统技术的发展综述[J].自动化仪表.2012,33(1)：1-8.
[20]朱勇,杨京燕,高领军,陈祥龙.含异步风力发电机的配电网无功优化规划研究[J].电力系统保护与控制.2012,40(5)：80-86.
[21]孙斌,姚海涛.基于PSO优化LSSVM的短期风速预测[J].电力系统保护与控制.2012,40(5)：85-89.
[22]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.
[23]Damousis Ioannis G., Alexiadis Minas C., Theocharis John B., Dokopoulos Petros S. A Fuzzy Model for Wind Speed Prediction and Power Generation in Wind Parks Using Spatial Correlation[J]. Energy Conversion.2004,19(2):352-361.
[24]Bilgili Mehmet, Sahin Besir, Yasar Abdulkadir. Application of Artificial Neural Networks for the Wind Speed Prediction of Target Station Using Reference Stations Data[J]. Renewable Energy.2007,32(14):2350-2360.
[25]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.
[26]Cadenas Erasmo, Rivera Wilfrido. Wind Speed Forecasting in Three Different Regions of Mexico, Using a Hybrid ARIMA-ANN Model[J]. Renewable Energy.2010,35(12): 2732-2738.
[27]刘烨,卢小芬,方瑞明,宋彦兵.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.
[28]Louka P., Galanis G., Siebert N., et al. Improvements in Wind Speed Forecasts for Wind Power Prediction Purposes Using Kalman Filtering[J]. Journal of Wind Engineering and Industrial Aerodynamics.2008,96(12):2348-2362.
[29]Bivona S., Bonanno G., Burlon R., Gurrera D., Leone C.. Stochastic Models for Wind Speed Forecasting[J]. Energy Conversion and Management.2011,52(2):1157-1165.
[30]Zhou Junyi, Shi Jing, Li Gong. Fine Tuning Support Vector Machines for Short-term Wind Speed Forecasting[J]. Energy Conversion and Management.2011,52(4):1990-1998.
[31]Li Gong, Shi Jing. On Comparing Three Artificial Neural Networks for Wind Speed Forecasting[J]. Applied Energy.2010,87(7):2313-2320.
[32]Huang Z., Chalabi Z.S. Use of Time-series Analysis to Model and Forecast Wind Speed[J]. Journal of Wind Engineering and Industrial Aerodynamics.1995,56(2-3):311-322.
[33]Nfaoui H., Buret J., Sayigh A.A.M. Stochastic Simulation of Hourly Average Wind Speed Sequences in Tangiers(Morocco)[J]. Solar Energy.1996,56(3):301-314.
[34]Alexiadis M.C., Dokopoulos P.S., Sahsamanoglou H.S., Manousaridis I.M. Short-term Forecasting of Wind Speed and Related Electrical Power[J]. Solar Energy.1998,63(1):61-68.
[35]Malmberg Anders, Holst Ulla, Holst Jan. Forecasting Near-surface Ocean Winds with Kalman Filter Techniques[J]. Ocean Engineering.2005,32(3-4):273-291.
[36]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[J].华东电力.2008,36(7)：26-29.
[37]Issac Armah Mensah,胡志刚,胡周军.一种基于时间序列模型的风速预测方法[J].计算技术与自动化.2010,29(2)：106-109.
[38]戚双斌,王维庆,张新燕.基于SVM的风速风功率预测模型[J].可再生能源.2010,28(4)：25-32.
[39]武小梅,白银明,文福拴.基于RBF神经元网络的风电功率短期预测[J].电力系统保护与控制.2011,39(15)：80-83.
[40]黄小华,李德源,吕文阁,成思源.基于人工神经网络模型的风速预测[J].太阳能学报.2011,32(2)：193-197.
[41]茆美琴,周松林,苏建徽.基于脊波神经网络的短期风电功率预测[J].电力系统自动化.2011,35(7)：70-74.
[42]胡琦,李元祥,宋金泽,褚宏莉.基于多普勒激光雷达的风场预测[J].激光与红外.2012,42(3)：268-273.
[43]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.
[44]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[J].电网技术.2008,32(7)：82-86.
[45]何育,高山,陈昊.基于ARMA-ARCH模型的风电场风速预测研究[J].江苏电机工程.2009,28(3)：1-3.
[46]李文良,卫志农,孙国强,完整,缪伟.基于改进空间相关法和径向基神经网络的风电场短期风速分时预测模型[J].电力自动化设备.2009,29(6)：89-92.
[47]Monfared Mohammad, Rastegar Hasan, Kojabadi Hossein Madadi. A New Strategy for Wind Speed Forecasting Using Artificial Intelligent Methods[J]. Renewable Energy.2009,34(3): 845-848.
[48]傅蓉,王维庆,何桂雄.基于气象因子的BP神经网络风电场风速预测[J].可再生能源.2009,27(5)：86-89.
[49]陶玉飞,李伟宏,杨喜峰.风电场风速预测模型研究[J].电网与能源清洁.2009,25(3)：53-56.
[50]栗然,王粤,肖进永.基于经验模式分解的风电场短期风速预测模型[J].中国电力.2009,42(9)：77-81.
[51]Liu Hui, Tian Hong-Qi, Chen Chao, Li Yan-fei. A Hybrid Statistical Method to Predict Wind Speed and Wind Power[J]. Renewable Energy.2010,35(8):1857-1861.
[52]王世谦,苏娟,杜松怀.基于数理统计及置信水平的短期风速预测修正方法研究[C].中国高等学校电力系统及其自动化专业第二十六届学术年会暨中国电机工程学会电力系统专业委员会2010年年会.上海：2010.
[53]李丽,叶林.基于改进持续法的短期风电功率预测[J].农业工程学报.2010,26(12)：182-187.
[54]Li Gong, Shi Jing, Zhou Junyi. Bayesian Adaptive Combination of Short-term Wind Speed Forecasts From Neural Network Models[J]. Renewable Energy.2011,36(1):352-359.
[55]Guo Zhenhai, Zhao Jing, Zhang Wenyu, Wang Jianzhou. A Corrected Hybrid Approach for Wind Speed Prediction in Hexi Corridor of China[J]. Energy.2011,36(3):1668-1679.
[56]彭怀午,刘方锐,杨晓峰.基于组合预测方法的风电场短期风速预测[J].太阳能学报.2011,32(4)：543-547.
[57]袁宇浩,龚松建,张广明.基于改进时间序列方法的短期风速预测研究[C].第二十二届中国过程控制会议.南京：2011.
[58]吕蓬,岳莉莉,赵晓丽.小波分解和ARIMA模型相结合的短期风速预测[J].科技信息.2011(14)：5-6.
[59]吴栋梁,王扬,郭创新,杨健.基于改进GMDH网络的风电场短期风速预测[J].电力系统与保护控制.2011,39(2)：88-93.
[60]罗文,王莉娜.风场短期风速预测研究[J].电工技术学报.2011,26(7)：68-74.
[61]王慧勤,雷刚.基于LIBSVM的风速预测方法研究[J].科学技术与工程.2011,11(22)：5440-5450.
[62]王莉,王德明,张广明,周献中.基于粗糙集和RBF神经网络的风电场短期风速预测模型[J].南京工业大学学报.2011,33(6)：67-71.
[63]方江晓,周晖,黄梅,T. S. Sidhu基于统计聚类分析的短期风电功率预测[J].电力系统保护与控制.2011,39(11)：67-73.
[64]刘辉,田红旗,Chen Chao,李燕飞.基于小波分析法与神经网络法的非平稳风速信号短期预测优化算法[J].中南大学学报.2011,42(9)：2704-2710.
[65]师洪涛,杨静玲,丁茂生,王金梅.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.
[66]秦剑,王建平,张崇巍.基于相空间重构小波神经网络的短期风速预测[J].电子测量与仪器预报.2012,26(3)：236-240.
[67]柳玉,郭全虎.基于AdaBoost与BP神经网络的风速预测研究[J].电网与清洁能源.2012,28(2)：80-89.
[68]王韶,杨江平,李逢兵,刘庭磊.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)：6-11.
[69]徐蓓蓓,蒋铁铮,易宏.基于GA-LS-SVM的风电场小时风速预测[J].水电与新能源.2012(1)：74-76.
[70]Zhang Wenyu, Wu Jie, Wang Jianzhou, et al. Performance Analysis of Four Modified Approaches for Wind Speed Forecasting[J]. Applied Energy.2012,99(November):324-333.
[71]袁春红,薛桁,杨振斌.近海区域风速数值模拟试验分析[J].太阳能学报.2004,25(6)：740-743.
[72]王晓兰,李辉.基于EMD分解的风电场风速和输出功率年度预测[J].太阳能学报.2011,32(3)：302-306.
[73]Wang Xiaochen, Guo Peng, Huang Xiaobin. A Review of Wind Power Forecasting Models[J]. Energy Procedia.2011(12):770-778.
[74]Foley Aoife M., Leahy Paul G., Marvuglia Antonino, McKeogh Eamon J. Current Methods and Advances in Forecasting of Wind Power Generation[J]. Renewable Energy.2012, 37(1):1-8.
[75]Landberg Lars, Watson Simon J. Short-term Prediction of Local Wind Conditions [J]. Boundary-Layer Meterology.1994,70(1-2):171-195.
[76]Pioson P., Kariniotakis G.N. Wind Power Forecasting Using Fuzzy Neural Network Enhanced with On-line Prediction Risk Assessment[C].2003 IEEE Bologna PowerTech-Conference Proceedings.2003,2:64-72.
[77]El-Fouly T.H.M, El-Saadany E.F., Salama M.M.A. One Day Ahead Prediction of Wind Speed Using Annual Trends[C].2006 IEEE Power Engineering Society General Meeting, PES. Ontario, Canada:2006.
[78]穆海振,徐家良,柯晓新,唐琳,陈德亮.高分辨率数值模式在风能资源评估中的应用初探[J].应用气象学报.2006,17(2)：152-159.
[79]孙永川.风电场风电功率短期预报技术研究[博士论文].兰州：兰州大学.2009.
[80]邓国卫,高晓清,惠小英,桂俊祥.酒泉地区风能资源开发优势度分析[J].高原气象.2010,29(6)：1634-1640.
[81]冯双磊,王伟胜,刘纯,戴慧珠.基于物理原理的风电场短期风速预测研究[J].太阳能学报.2011,32(5)：611-616.
[82]陈玲,赖旭,刘霄,陈秋华.WRF模式在风电场风速预测中的应用[J].武汉大学学报.2012,45(1)：103-106.
[83]王健,严干贵,宋薇,穆钢.风电功率预测技术综述[J].东北电力大学学报.2011,31(3)：20-24.
[84]Ma Lei, Luan Shiyan, Jiang Chuanwen, Liu Hongling, Zhang Yan. A Review on the Forecasting of Wind Speed and Generated Power[J]. Renewable and Sustainable Energy Reviews.2009,13(4):915-920.
[85]赵攀,戴义平,夏俊荣,盛迎新.卡尔曼滤波修正的风电场短期功率预测模型[J].西安交通大学学报.2011,45(5)：47-51.
[86]李洪涛,马志勇,芮晓明.基于数值天气预报的风能预测系统[J].中国电力.2012,45(2)：64-68.
[87]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[J].华东电力.2008,36(7)：26-29.
[88]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.
[89]千燕.应用时间序列分析[M].北京：中国人民大学出版社.2005.
[90]Frandsen S.T., Jorgensen H.E., Rathmann Ole, et al. The Making of A Second-generation Wind Farm Efficiency Model Complex[J]. Wind Energy.2009,12(5):445-458.
[91]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[J].中国机电工程学报.2005,25(10)：107-110.
[92]龚强,袁国恩,张云秋,汪宏宇,于华深,蔺娜,白乐生.MM5模式在风能资源普查中的应用试验[J].资源科学.2006,28(1)：145-150.
[93]王毅荣.河西走廊风能资源立体分布研究[J].太阳能学报.2007,28(4)：451-456.
[94]杨振斌,朱瑞兆,薛桁.风电场风能资源评价两个新参数---相当风速、有功风功率密度[J].太阳能学报.2007,28(3)：248-251.
[95]杜燕军,冯长青.风电场代表年风速计算方法的分析[J].可再生能源.2010,28(1)：105-108.
[96]陈练,李栋梁,吴洪宝.中国风速概率分布及在风能评估中的应用[J].太阳能学报.2010,31(9)：1209-1214.
[97]彭怀午,冯长青,包紫光.风资源评价中风切变指数的研究[J].可再生能源.2010,28(1)：21-23.
[2]Nikhil Chaudhry, Larry Hughes. Forecasting the Reliability of Wind-energy Systems:A New Approach Using the RL Technique[J]. Applied Energy.2012,96(August):422-430.
[3]Saidur, R., Islam, M.R., Rahim, N.A., Solangi, K.H. A Review on Global Wind Energy Policy[J]. Renewable and Sustainable Energy Reviews.2010,14(7):1744-1762.
[4]Piotr Michalak, Jacek Zimny. Wind Energy Development in The World, Europe and Poland From 1995 to 2009:Current Status and Future Perspectives[J]. Renewable and Sustainable Energy Reviews.2011,15(5):2330-2341.
[5]朱成章.关于中国风能资源储量的质疑[J].中外能源.2010,15(4)：34-39.
[6]杨振斌.中国风能资源[J].大自然.2007(2)：48-49.
[7]中国可再生能源学会风能专业委员会.2011年中国风电装机容量统计.北京：2012.
[8]McVicar, T.R., Van Niel, T.G., Li L.T.,et al. Wind Speed Climatology and Trends for Australia,1975-2006:Capturing the Stilling Phenomenon and Comparison with Near-surface Reanalysis Output[J]. Geophysical Research Letters.2008,35(20):L20403.
[9]Sahin Ahmet Duran. Progress and Recent Trends in Wind Energy[J]. Progress in Energy and Combusion Science.2004,30(5):501-543.
[10]Sesto Ezio, Casale Claudio. Exploitation of Wind As an Energy Source to Meet the World's Electricity Demand[J]. Journal of Wind Energy and Industrial Aerodynamics.1998, 74-76(April):375-387.
[11]李振朝,韦志刚,高荣.近50年河西绿洲地面风的时空变化特征[J].高原气象.2004,23(2)：259-263.
[12]刘苏峡,邱建秀,莫兴国.华北平原1951年至2006年风速变化特征分析[J].资源科学.2009,31(9)：1486-1492.
[13]Mahrt Larry. Surface Wind Direction Variability [J]. Journal of Applied Meteorlogy and Climatology.2010,50(1):144-152.
[14]Kose Ramazan, Arif Ozgur M., Erbas Oguzhan, Tugcu Abtullah. The Analysis of Wind Data and Wind Energy Potential in Kutahya,Turkey[J]. Renewable and Sustainable Energy Reviews.2004,8(3):277-288.
[15]张德,朱蓉,罗勇,俞卫,王澄海.风能模拟系统WEST在中国风能数值模拟中的应用[J].高原气象.2008,27(1)：202-207.
[16]朱飙,李春华,陆登荣.甘肃酒泉区域风能资源评估[J].干旱气象.2009,27(2)：152-156.
[17]田德,王海宽,韩巧丽.浓缩风能型风力发电机的研究与进展[J].农业工程学报,2003,19(Z1)：177-181.
[18]刘细平,林鹤云.风力发电机及风力发电控制技术综述[J].大电机技术.2007(3)：17-20.
[19]程启明,程尹曼,王映斐,汪明媚.风力发电系统技术的发展综述[J].自动化仪表.2012,33(1)：1-8.
[20]朱勇,杨京燕,高领军,陈祥龙.含异步风力发电机的配电网无功优化规划研究[J].电力系统保护与控制.2012,40(5)：80-86.
[21]孙斌,姚海涛.基于PSO优化LSSVM的短期风速预测[J].电力系统保护与控制.2012,40(5)：85-89.
[22]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.
[23]Damousis Ioannis G., Alexiadis Minas C., Theocharis John B., Dokopoulos Petros S. A Fuzzy Model for Wind Speed Prediction and Power Generation in Wind Parks Using Spatial Correlation[J]. Energy Conversion.2004,19(2):352-361.
[24]Bilgili Mehmet, Sahin Besir, Yasar Abdulkadir. Application of Artificial Neural Networks for the Wind Speed Prediction of Target Station Using Reference Stations Data[J]. Renewable Energy.2007,32(14):2350-2360.
[25]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.
[26]Cadenas Erasmo, Rivera Wilfrido. Wind Speed Forecasting in Three Different Regions of Mexico, Using a Hybrid ARIMA-ANN Model[J]. Renewable Energy.2010,35(12): 2732-2738.
[27]刘烨,卢小芬,方瑞明,宋彦兵.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.
[28]Louka P., Galanis G., Siebert N., et al. Improvements in Wind Speed Forecasts for Wind Power Prediction Purposes Using Kalman Filtering[J]. Journal of Wind Engineering and Industrial Aerodynamics.2008,96(12):2348-2362.
[29]Bivona S., Bonanno G., Burlon R., Gurrera D., Leone C.. Stochastic Models for Wind Speed Forecasting[J]. Energy Conversion and Management.2011,52(2):1157-1165.
[30]Zhou Junyi, Shi Jing, Li Gong. Fine Tuning Support Vector Machines for Short-term Wind Speed Forecasting[J]. Energy Conversion and Management.2011,52(4):1990-1998.
[31]Li Gong, Shi Jing. On Comparing Three Artificial Neural Networks for Wind Speed Forecasting[J]. Applied Energy.2010,87(7):2313-2320.
[32]Huang Z., Chalabi Z.S. Use of Time-series Analysis to Model and Forecast Wind Speed[J]. Journal of Wind Engineering and Industrial Aerodynamics.1995,56(2-3):311-322.
[33]Nfaoui H., Buret J., Sayigh A.A.M. Stochastic Simulation of Hourly Average Wind Speed Sequences in Tangiers(Morocco)[J]. Solar Energy.1996,56(3):301-314.
[34]Alexiadis M.C., Dokopoulos P.S., Sahsamanoglou H.S., Manousaridis I.M. Short-term Forecasting of Wind Speed and Related Electrical Power[J]. Solar Energy.1998,63(1):61-68.
[35]Malmberg Anders, Holst Ulla, Holst Jan. Forecasting Near-surface Ocean Winds with Kalman Filter Techniques[J]. Ocean Engineering.2005,32(3-4):273-291.
[36]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[J].华东电力.2008,36(7)：26-29.
[37]Issac Armah Mensah,胡志刚,胡周军.一种基于时间序列模型的风速预测方法[J].计算技术与自动化.2010,29(2)：106-109.
[38]戚双斌,王维庆,张新燕.基于SVM的风速风功率预测模型[J].可再生能源.2010,28(4)：25-32.
[39]武小梅,白银明,文福拴.基于RBF神经元网络的风电功率短期预测[J].电力系统保护与控制.2011,39(15)：80-83.
[40]黄小华,李德源,吕文阁,成思源.基于人工神经网络模型的风速预测[J].太阳能学报.2011,32(2)：193-197.
[41]茆美琴,周松林,苏建徽.基于脊波神经网络的短期风电功率预测[J].电力系统自动化.2011,35(7)：70-74.
[42]胡琦,李元祥,宋金泽,褚宏莉.基于多普勒激光雷达的风场预测[J].激光与红外.2012,42(3)：268-273.
[43]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.
[44]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[J].电网技术.2008,32(7)：82-86.
[45]何育,高山,陈昊.基于ARMA-ARCH模型的风电场风速预测研究[J].江苏电机工程.2009,28(3)：1-3.
[46]李文良,卫志农,孙国强,完整,缪伟.基于改进空间相关法和径向基神经网络的风电场短期风速分时预测模型[J].电力自动化设备.2009,29(6)：89-92.
[47]Monfared Mohammad, Rastegar Hasan, Kojabadi Hossein Madadi. A New Strategy for Wind Speed Forecasting Using Artificial Intelligent Methods[J]. Renewable Energy.2009,34(3): 845-848.
[48]傅蓉,王维庆,何桂雄.基于气象因子的BP神经网络风电场风速预测[J].可再生能源.2009,27(5)：86-89.
[49]陶玉飞,李伟宏,杨喜峰.风电场风速预测模型研究[J].电网与能源清洁.2009,25(3)：53-56.
[50]栗然,王粤,肖进永.基于经验模式分解的风电场短期风速预测模型[J].中国电力.2009,42(9)：77-81.
[51]Liu Hui, Tian Hong-Qi, Chen Chao, Li Yan-fei. A Hybrid Statistical Method to Predict Wind Speed and Wind Power[J]. Renewable Energy.2010,35(8):1857-1861.
[52]王世谦,苏娟,杜松怀.基于数理统计及置信水平的短期风速预测修正方法研究[C].中国高等学校电力系统及其自动化专业第二十六届学术年会暨中国电机工程学会电力系统专业委员会2010年年会.上海：2010.
[53]李丽,叶林.基于改进持续法的短期风电功率预测[J].农业工程学报.2010,26(12)：182-187.
[54]Li Gong, Shi Jing, Zhou Junyi. Bayesian Adaptive Combination of Short-term Wind Speed Forecasts From Neural Network Models[J]. Renewable Energy.2011,36(1):352-359.
[55]Guo Zhenhai, Zhao Jing, Zhang Wenyu, Wang Jianzhou. A Corrected Hybrid Approach for Wind Speed Prediction in Hexi Corridor of China[J]. Energy.2011,36(3):1668-1679.
[56]彭怀午,刘方锐,杨晓峰.基于组合预测方法的风电场短期风速预测[J].太阳能学报.2011,32(4)：543-547.
[57]袁宇浩,龚松建,张广明.基于改进时间序列方法的短期风速预测研究[C].第二十二届中国过程控制会议.南京：2011.
[58]吕蓬,岳莉莉,赵晓丽.小波分解和ARIMA模型相结合的短期风速预测[J].科技信息.2011(14)：5-6.
[59]吴栋梁,王扬,郭创新,杨健.基于改进GMDH网络的风电场短期风速预测[J].电力系统与保护控制.2011,39(2)：88-93.
[60]罗文,王莉娜.风场短期风速预测研究[J].电工技术学报.2011,26(7)：68-74.
[61]王慧勤,雷刚.基于LIBSVM的风速预测方法研究[J].科学技术与工程.2011,11(22)：5440-5450.
[62]王莉,王德明,张广明,周献中.基于粗糙集和RBF神经网络的风电场短期风速预测模型[J].南京工业大学学报.2011,33(6)：67-71.
[63]方江晓,周晖,黄梅,T. S. Sidhu基于统计聚类分析的短期风电功率预测[J].电力系统保护与控制.2011,39(11)：67-73.
[64]刘辉,田红旗,Chen Chao,李燕飞.基于小波分析法与神经网络法的非平稳风速信号短期预测优化算法[J].中南大学学报.2011,42(9)：2704-2710.
[65]师洪涛,杨静玲,丁茂生,王金梅.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.
[66]秦剑,王建平,张崇巍.基于相空间重构小波神经网络的短期风速预测[J].电子测量与仪器预报.2012,26(3)：236-240.
[67]柳玉,郭全虎.基于AdaBoost与BP神经网络的风速预测研究[J].电网与清洁能源.2012,28(2)：80-89.
[68]王韶,杨江平,李逢兵,刘庭磊.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)：6-11.
[69]徐蓓蓓,蒋铁铮,易宏.基于GA-LS-SVM的风电场小时风速预测[J].水电与新能源.2012(1)：74-76.
[70]Zhang Wenyu, Wu Jie, Wang Jianzhou, et al. Performance Analysis of Four Modified Approaches for Wind Speed Forecasting[J]. Applied Energy.2012,99(November):324-333.
[71]袁春红,薛桁,杨振斌.近海区域风速数值模拟试验分析[J].太阳能学报.2004,25(6)：740-743.
[72]王晓兰,李辉.基于EMD分解的风电场风速和输出功率年度预测[J].太阳能学报.2011,32(3)：302-306.
[73]Wang Xiaochen, Guo Peng, Huang Xiaobin. A Review of Wind Power Forecasting Models[J]. Energy Procedia.2011(12):770-778.
[74]Foley Aoife M., Leahy Paul G., Marvuglia Antonino, McKeogh Eamon J. Current Methods and Advances in Forecasting of Wind Power Generation[J]. Renewable Energy.2012, 37(1):1-8.
[75]Landberg Lars, Watson Simon J. Short-term Prediction of Local Wind Conditions [J]. Boundary-Layer Meterology.1994,70(1-2):171-195.
[76]Pioson P., Kariniotakis G.N. Wind Power Forecasting Using Fuzzy Neural Network Enhanced with On-line Prediction Risk Assessment[C].2003 IEEE Bologna PowerTech-Conference Proceedings.2003,2:64-72.
[77]El-Fouly T.H.M, El-Saadany E.F., Salama M.M.A. One Day Ahead Prediction of Wind Speed Using Annual Trends[C].2006 IEEE Power Engineering Society General Meeting, PES. Ontario, Canada:2006.
[78]穆海振,徐家良,柯晓新,唐琳,陈德亮.高分辨率数值模式在风能资源评估中的应用初探[J].应用气象学报.2006,17(2)：152-159.
[79]孙永川.风电场风电功率短期预报技术研究[博士论文].兰州：兰州大学.2009.
[80]邓国卫,高晓清,惠小英,桂俊祥.酒泉地区风能资源开发优势度分析[J].高原气象.2010,29(6)：1634-1640.
[81]冯双磊,王伟胜,刘纯,戴慧珠.基于物理原理的风电场短期风速预测研究[J].太阳能学报.2011,32(5)：611-616.
[82]陈玲,赖旭,刘霄,陈秋华.WRF模式在风电场风速预测中的应用[J].武汉大学学报.2012,45(1)：103-106.
[83]王健,严干贵,宋薇,穆钢.风电功率预测技术综述[J].东北电力大学学报.2011,31(3)：20-24.
[84]Ma Lei, Luan Shiyan, Jiang Chuanwen, Liu Hongling, Zhang Yan. A Review on the Forecasting of Wind Speed and Generated Power[J]. Renewable and Sustainable Energy Reviews.2009,13(4):915-920.
[85]赵攀,戴义平,夏俊荣,盛迎新.卡尔曼滤波修正的风电场短期功率预测模型[J].西安交通大学学报.2011,45(5)：47-51.
[86]李洪涛,马志勇,芮晓明.基于数值天气预报的风能预测系统[J].中国电力.2012,45(2)：64-68.
[87]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[J].华东电力.2008,36(7)：26-29.
[88]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.
[89]千燕.应用时间序列分析[M].北京：中国人民大学出版社.2005.
[90]Frandsen S.T., Jorgensen H.E., Rathmann Ole, et al. The Making of A Second-generation Wind Farm Efficiency Model Complex[J]. Wind Energy.2009,12(5):445-458.
[91]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[J].中国机电工程学报.2005,25(10)：107-110.
[92]龚强,袁国恩,张云秋,汪宏宇,于华深,蔺娜,白乐生.MM5模式在风能资源普查中的应用试验[J].资源科学.2006,28(1)：145-150.
[93]王毅荣.河西走廊风能资源立体分布研究[J].太阳能学报.2007,28(4)：451-456.
[94]杨振斌,朱瑞兆,薛桁.风电场风能资源评价两个新参数---相当风速、有功风功率密度[J].太阳能学报.2007,28(3)：248-251.
[95]杜燕军,冯长青.风电场代表年风速计算方法的分析[J].可再生能源.2010,28(1)：105-108.
[96]陈练,李栋梁,吴洪宝.中国风速概率分布及在风能评估中的应用[J].太阳能学报.2010,31(9)：1209-1214.
[97]彭怀午,冯长青,包紫光.风资源评价中风切变指数的研究[J].可再生能源.2010,28(1)：21-23.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |