风电场内机组优化调度研究
|
摘要
随着电力系统中风电并网比例的增加,风能的随机波动性对传统电力系统经济调度和安全运行带来挑战。研究在风电功率预测与电力系统的负荷约束条件下,风电场内机组优化调度问题,不仅能减少风力发电机组的冗余运行和磨损浪费,避免机组的频繁启停,还可以降低运行成本,提高风电场输出功率的电能质量,有效减轻风电波动性对电网的影响,从而在保证电力系统安全性的前提下,提高电力系统的消纳风电能力和经济效益。
以风电场功率预测数据为基础,重点研究了以降低风力发电机组疲劳载荷损伤相对量和降低集电系统损耗为目标的风电场内机组优化调度的算法,完成了以下研究工作。
(1)提出了不同运行工况下风力发电机组关键部件相对疲劳损伤量的计算方法。根据华北某风电场的风资源数据,利用瑞利分布的风速累积分布函数,基于GH-Bladed模拟了1.5MW风力发电机组的疲劳载荷,利用雨流循环计数法,得到风力发电机组各个部件的疲劳载荷谱,然后根据仿真计算和Miner法则得到的风力发电机组关键部件的相对疲劳损伤量,可为风电场内机组优化运行提供评价准则。
(2)基于相空间重构的神经网络风电功率预测算法的应用。风电场内机组优化调度是以风力发电机组的短期和超短期功率预测值为研究基础,根据混沌-相空间重构的原理可知风力发电机组的风速和风电功率时间序列数据具有混沌的属性的基础上,将相空间重构与神经网络相结合,建立混沌-Elman、混沌-BP和混沌-Volterra级数的风电功率预测模型,经实例验证,分析比较得出混沌-Elman模型的预测效果相对较好,能够提高预测的精度和稳定性。
(3)建立以风电场集电系统网损最小为目标的机组优化调度模型。以风电场内集电系统网损最小为目标函数,电网调度要求、风力发电机组有功输出的功率上下限、风力发电机组无功输出的功率上下限、风力发电机组端电压上下限、变压器变比上下限等为约束条件,建立机组优化调度的数学模型,分别采用粒子群优化算法和粒子群-遗传优化算法进行寻优。结果表明,粒子群-遗传算法在优化效果和运算效率方面均优于单一粒子群算法。
(4)建立了以风电场内机械损伤量最小为目标的机组组合优化模型。基于前述的相对疲劳损伤量模型,建立机组组合模型,合理配置机组启停方案,以期在调度期内风电场整体机械损伤最小,延长机组运行效率和使用寿命。然后利用改进二进制粒子群优化算法(BPSO)、遗传优化算法(GA)、粒子群-遗传混合优化算法(BPSO-GA),进行优化求解。结果表明,BPSO-GA比单一GA和BPSO提高了优化性能,运行期间总疲劳损伤量最小；引入粒子群优化参数的BPSO-GA算法的计算时长相对BPSO算法略长,但比GA算法计算时长要短；三种模型的计算时长从大到小依次为：GA, BPSO-GA, BPSO。
(5)建立了基于机组优先级分类的风电场内功率分配模型。以风力发电机组发电功率、风速平均值和均方根差值作为特征值,分析机组发电性能,并分别采用SOFM神经网络算法与基于模拟退火遗传算法的模糊C均值聚类算法建立机组优先级分类模型。将发电性能较好的一类作为优先执行发电计划的机组,计及线路损耗后的发电计划,对风电场内其余机组进行两层优化,外层是以风力发电机组相对疲劳损伤量最小为目标的机组优化出力,内层是确定机组间负荷满足电网调度要求的最优功率分配。通过两层分配,得到既满足电网调度需求,又降低风电场运行损耗的功率分配结果。结果表明,基于遗传模拟退火算法的模糊聚类算法分类方法的疲劳损伤量比自组织特征映射神经网络分类方法的疲劳损伤量较小,SAGA-FCM分类方法下停机的机组台数较多。风力发电机组分类后优化调度,能够使风电场机组运行优化,提高风电场输出电能质量。
With the increase of wind power integration in power system, the stochastic variability of wind energy has been posing great challenges on traditional economic dispatch and operational security. Research on optimal dispatch in a wind farm with constrains on power system load and wind power forecasting could not only reduce operational redundancy and mechanical abrasion of wind turbines, avoid frequent on-off operation, but also decrease operation cost, improve power quality. All these above would eventually mitigate the negative impacts from wind power on power system, thus to maximize wind power penetration and economic benefits on the condition of secure system operation.
Based on the wind power forecasting data, this dissertation mainly discussed the unit optimal dispatch algorithms aiming to reduce the wind turbine relative fatigue loading damage and the line loss of collection system in the wind farm. The main contribution of the dissertation can be summarized as follows:
(1) A relative fatigue loss method for wind turbine key components in different operation conditions is established. Based on the wind resource data of a wind farm in Northern China, with the Rayleigh distribution of the wind speed cumulative distribution function, this dissertation simulated the fatigue load of1.5MW wind turbine using GH-Bladed and obtained the fatigue load spectrum of wind turbine components using the rain flow cycle count method. Then, according to the Miner laws and the simulation calculation, the relative fatigue damage of the wind turbine key components was calculated, providing evaluation criterion for unit optimal dispatch in the wind farm.
(2) A wind power forecasting model is built based on neural network with the phase space reconstruction. Unit optimal dispatch in the wind farm is based on the wind turbine short-term and ultra short-term power forecasting. According to the principle of chaos-phase space reconstruction, it can be seen that the wind speed and the wind power time series data of wind turbines have the property of chaotic. On this basis, the phase space reconstruction was combined with neural network to establish chaos-BP and chaos-Elman, chaos-Volterra series wind power forecasting models. Tests showed that the forecasts of Elman model are more accurate than that of the others, improving accuracy and stability of the forecasting.
(3) An unit optimal dispatch model is established minimizing the losses of collection system in a wind farm. With constraint conditions including wind farm output's satisfaction of grid dispatch, upper and lower limit of the wind generator active power output, upper and lower limit of the wind generator reactive power output power, upper and lower limit of the wind generator voltage and upper and lower limit of the transformer ratio, with the minimum collecting system network loss in the wind farm being the objective function, the mathematical model of optimal dispatch was established by using particle swarm optimization algorithm and genetic-particle swarm optimization algorithm respectively. The results showed that genetic-particle swarm optimization algorithm was better than particle swarm optimization algorithm in both optimization performance and computing efficiency.
(4) A unit commitment model is established with an objective function of minimization of mechanical losses in a wind farm. Based on relative fatigue damage model built before, unit commitment model was established for the rational allocation of unit start-up in wind farm, which could obtain the minimum mechanical damage in the scheduling period, prolong the operation efficiency and life. And then the improved binary particle swarm optimization algorithm, genetic optimization algorithm, genetic-particle swarm optimization algorithm were utilized to obtain an optimized solution. The results showed that BPSO-GA improved the optimal performance better than GA and BPSO, and minimized the amount of fatigue damage in runtime. The calculation time of the BPSO-GA algorithm which introduced the particle swarm optimization parameters was slightly longer than that of the BPSO algorithm, but shorter than that of the GA algorithm. The calculation time of three models in decreasing order is:GA, BPSO-GA, BPSO.
(5) A model for optimally allocating wind turbine output is presented based on units classification. After the analysis of large amount of historical data of unit in the wind farm, the average and root mean square difference of each wind turbine power and the wind speed were extracted as the characteristic value to analyze the unit performance, the units were classified with the SOFM neural network algorithm and the fuzzy c-means clustering algorithm based on simulated annealing and genetic algorithm. The units with better electrical performance in the classification were given priority in the execution of the power generation plan. Considering the line loss of the power generation plan, the rest of the units in the wind farm were optimized in two layers, the outer layer being the unit commitment optimization with the target of minimum relative fatigue damage of wind turbine, the inner layer being the optimal power dispatch of the units satisfying the grid requirement. Finally the power allocation model with the output of a wind farm in compliance with the dispatch requirements of the grid was established. Both the two kinds of classification algorithm can get unit commitments meeting the requirements of power grid dispatch. After comparison, however, it is found that the fatigue damage value of fuzzy clustering algorithm based on genetic simulated annealing algorithm is smaller than that of the SOFM neural network algorithm. Optimal dispatch based on wind turbines classification could optimize units operation in wind farm and improve the power quality of wind farm.
以风电场功率预测数据为基础,重点研究了以降低风力发电机组疲劳载荷损伤相对量和降低集电系统损耗为目标的风电场内机组优化调度的算法,完成了以下研究工作。
(1)提出了不同运行工况下风力发电机组关键部件相对疲劳损伤量的计算方法。根据华北某风电场的风资源数据,利用瑞利分布的风速累积分布函数,基于GH-Bladed模拟了1.5MW风力发电机组的疲劳载荷,利用雨流循环计数法,得到风力发电机组各个部件的疲劳载荷谱,然后根据仿真计算和Miner法则得到的风力发电机组关键部件的相对疲劳损伤量,可为风电场内机组优化运行提供评价准则。
(2)基于相空间重构的神经网络风电功率预测算法的应用。风电场内机组优化调度是以风力发电机组的短期和超短期功率预测值为研究基础,根据混沌-相空间重构的原理可知风力发电机组的风速和风电功率时间序列数据具有混沌的属性的基础上,将相空间重构与神经网络相结合,建立混沌-Elman、混沌-BP和混沌-Volterra级数的风电功率预测模型,经实例验证,分析比较得出混沌-Elman模型的预测效果相对较好,能够提高预测的精度和稳定性。
(3)建立以风电场集电系统网损最小为目标的机组优化调度模型。以风电场内集电系统网损最小为目标函数,电网调度要求、风力发电机组有功输出的功率上下限、风力发电机组无功输出的功率上下限、风力发电机组端电压上下限、变压器变比上下限等为约束条件,建立机组优化调度的数学模型,分别采用粒子群优化算法和粒子群-遗传优化算法进行寻优。结果表明,粒子群-遗传算法在优化效果和运算效率方面均优于单一粒子群算法。
(4)建立了以风电场内机械损伤量最小为目标的机组组合优化模型。基于前述的相对疲劳损伤量模型,建立机组组合模型,合理配置机组启停方案,以期在调度期内风电场整体机械损伤最小,延长机组运行效率和使用寿命。然后利用改进二进制粒子群优化算法(BPSO)、遗传优化算法(GA)、粒子群-遗传混合优化算法(BPSO-GA),进行优化求解。结果表明,BPSO-GA比单一GA和BPSO提高了优化性能,运行期间总疲劳损伤量最小；引入粒子群优化参数的BPSO-GA算法的计算时长相对BPSO算法略长,但比GA算法计算时长要短；三种模型的计算时长从大到小依次为：GA, BPSO-GA, BPSO。
(5)建立了基于机组优先级分类的风电场内功率分配模型。以风力发电机组发电功率、风速平均值和均方根差值作为特征值,分析机组发电性能,并分别采用SOFM神经网络算法与基于模拟退火遗传算法的模糊C均值聚类算法建立机组优先级分类模型。将发电性能较好的一类作为优先执行发电计划的机组,计及线路损耗后的发电计划,对风电场内其余机组进行两层优化,外层是以风力发电机组相对疲劳损伤量最小为目标的机组优化出力,内层是确定机组间负荷满足电网调度要求的最优功率分配。通过两层分配,得到既满足电网调度需求,又降低风电场运行损耗的功率分配结果。结果表明,基于遗传模拟退火算法的模糊聚类算法分类方法的疲劳损伤量比自组织特征映射神经网络分类方法的疲劳损伤量较小,SAGA-FCM分类方法下停机的机组台数较多。风力发电机组分类后优化调度,能够使风电场机组运行优化,提高风电场输出电能质量。
With the increase of wind power integration in power system, the stochastic variability of wind energy has been posing great challenges on traditional economic dispatch and operational security. Research on optimal dispatch in a wind farm with constrains on power system load and wind power forecasting could not only reduce operational redundancy and mechanical abrasion of wind turbines, avoid frequent on-off operation, but also decrease operation cost, improve power quality. All these above would eventually mitigate the negative impacts from wind power on power system, thus to maximize wind power penetration and economic benefits on the condition of secure system operation.
Based on the wind power forecasting data, this dissertation mainly discussed the unit optimal dispatch algorithms aiming to reduce the wind turbine relative fatigue loading damage and the line loss of collection system in the wind farm. The main contribution of the dissertation can be summarized as follows:
(1) A relative fatigue loss method for wind turbine key components in different operation conditions is established. Based on the wind resource data of a wind farm in Northern China, with the Rayleigh distribution of the wind speed cumulative distribution function, this dissertation simulated the fatigue load of1.5MW wind turbine using GH-Bladed and obtained the fatigue load spectrum of wind turbine components using the rain flow cycle count method. Then, according to the Miner laws and the simulation calculation, the relative fatigue damage of the wind turbine key components was calculated, providing evaluation criterion for unit optimal dispatch in the wind farm.
(2) A wind power forecasting model is built based on neural network with the phase space reconstruction. Unit optimal dispatch in the wind farm is based on the wind turbine short-term and ultra short-term power forecasting. According to the principle of chaos-phase space reconstruction, it can be seen that the wind speed and the wind power time series data of wind turbines have the property of chaotic. On this basis, the phase space reconstruction was combined with neural network to establish chaos-BP and chaos-Elman, chaos-Volterra series wind power forecasting models. Tests showed that the forecasts of Elman model are more accurate than that of the others, improving accuracy and stability of the forecasting.
(3) An unit optimal dispatch model is established minimizing the losses of collection system in a wind farm. With constraint conditions including wind farm output's satisfaction of grid dispatch, upper and lower limit of the wind generator active power output, upper and lower limit of the wind generator reactive power output power, upper and lower limit of the wind generator voltage and upper and lower limit of the transformer ratio, with the minimum collecting system network loss in the wind farm being the objective function, the mathematical model of optimal dispatch was established by using particle swarm optimization algorithm and genetic-particle swarm optimization algorithm respectively. The results showed that genetic-particle swarm optimization algorithm was better than particle swarm optimization algorithm in both optimization performance and computing efficiency.
(4) A unit commitment model is established with an objective function of minimization of mechanical losses in a wind farm. Based on relative fatigue damage model built before, unit commitment model was established for the rational allocation of unit start-up in wind farm, which could obtain the minimum mechanical damage in the scheduling period, prolong the operation efficiency and life. And then the improved binary particle swarm optimization algorithm, genetic optimization algorithm, genetic-particle swarm optimization algorithm were utilized to obtain an optimized solution. The results showed that BPSO-GA improved the optimal performance better than GA and BPSO, and minimized the amount of fatigue damage in runtime. The calculation time of the BPSO-GA algorithm which introduced the particle swarm optimization parameters was slightly longer than that of the BPSO algorithm, but shorter than that of the GA algorithm. The calculation time of three models in decreasing order is:GA, BPSO-GA, BPSO.
(5) A model for optimally allocating wind turbine output is presented based on units classification. After the analysis of large amount of historical data of unit in the wind farm, the average and root mean square difference of each wind turbine power and the wind speed were extracted as the characteristic value to analyze the unit performance, the units were classified with the SOFM neural network algorithm and the fuzzy c-means clustering algorithm based on simulated annealing and genetic algorithm. The units with better electrical performance in the classification were given priority in the execution of the power generation plan. Considering the line loss of the power generation plan, the rest of the units in the wind farm were optimized in two layers, the outer layer being the unit commitment optimization with the target of minimum relative fatigue damage of wind turbine, the inner layer being the optimal power dispatch of the units satisfying the grid requirement. Finally the power allocation model with the output of a wind farm in compliance with the dispatch requirements of the grid was established. Both the two kinds of classification algorithm can get unit commitments meeting the requirements of power grid dispatch. After comparison, however, it is found that the fatigue damage value of fuzzy clustering algorithm based on genetic simulated annealing algorithm is smaller than that of the SOFM neural network algorithm. Optimal dispatch based on wind turbines classification could optimize units operation in wind farm and improve the power quality of wind farm.
引文
[1]Global Wind Energy Council. The Global Wind Energy Statistics 2013[R]. 2014
[2]中国可再生能源学会风能专业委员会.2013年风电装机容量统计[R].2014
[3]孙金龙,田海峰.风电场并网对电力系统的影响分析[J].太阳能,2008,10：30-32
[4]Landberg L, Watson SJ. Short term Prediction of local wind conditions[J]. Bounddary-Layer Meteorology,1994,70(1):171-195
[5]胡朝阳,王亮,郭强.华东电网大规模风电调度运行的研究[J].华东电力,2010,38(6)：0785-0788
[6]程道平.风电场的并网运行问题[J].上海节能,2008,10：34-37
[7]栾士岩,张焰,蒋传文,俞国勤,林一.含风电场的电力系统节能减排优化调度研究[J].华东电力,2010,38(1)：39-43
[8]邓恩思.大型风电场运行的特点及并网运行的问题[J].能源研究与利用,2010,4：8-11
[9]贵州电力试验研究院,华北电力科学研究院有限责任公司组编,雷铭主编.电力网降损节能手册[M].北京：中国电力出版社,2005：181-183
[10]IEC61400-1, Wind Turbine Generator Systems-Part 1:Safety Requirements[S]. Second edition 1999-02. Geneva:International Electrotechnical Commission, 1999
[11]IEC61400-1, Wind Turbines-Part 1:Design Requirements[S].Thirdedition 2005-08. Geneva:International Electrotechnical Commission,2005
[12]GL 2010 IV-part 1, Rules and Guidelines, Iv-Industrial Services, Guideline for the Certification of Wind Turbines[S].Edition 2010. Hamburg:Germanischer Lloyd WindEnergie GmbH,2010
[13]郭亮.风力发电机组疲劳载荷测试系统的研究与设计[D].乌鲁木齐：新疆大学,2006
[14]N. INOMATA, K. TSUCHIYA and S. YAM ADA. Measurement of stress on blade of NED03 500Kw prototype wind turbine[J].Renewable Energy,1999 (16):912-915
[15]M.Noda, R.G.J.Flay.A simulation model for wind turbine blade fatigue loads[J].Journal of Wind Engineering and Industrial Aerodynamics,1999, (83):527-540
[16]岳勇.风力发电机组机械零部件抗疲劳设计方法的研究[D].乌鲁木齐：新疆农业大学,2005
[17]韩玉清,吕路勇,邢晓坡.风轮叶片叶根疲劳强度分析方法的研究[J].能源工程,2009,(3)：33-36
[18]张峥,陈欣.风力机疲劳问题分析[J].华北水利水电学院学报,2008,29(3)：41-43
[19]张锦源.风力机可靠性的研究[D].汕头：汕头大学,2006
[20]宋聚众.1.5MW变速恒频风力发电机组气动与结构设计技术[D].汕头：汕头大学,2007
[21]孙靖宇.基于永磁同步电机的电动变桨研究[D].兰州：兰州交通大学,2010
[22]刘雄辉,李德源,吕文阁,成思源.水平轴风力机轮毂强度和疲劳寿命分析方法研究[J].太阳能学报,2012,33(5)：763-767
[23]王平,曹家勇,张执南.大型风力发电机轮毂强度的有限元分析与应用[J].华北电力,2009,37(7)：1206-1208
[24]Wang Ping, Cao Jiayong, Zhang Zhinan.Finite element analysis for strength of wheel hubs of large wind turbines[J].East China Electric Power,2009,37(7): 1206-1208
[25]孙传宗,姚兴佳,单光坤,等.MW级风力发电机轮毂强度分析[J].沈阳工业大学学报,2008,30(1)：46-49
[26]Brent L. Ellis & L. Montgomery Smith. Modeling and experimental testing of strain gauges in operational and failure modes. IEEE Transactions on instrumentation and measurement,2009,58 (7):1-2.
[27]邓良.大型风力发电机组轮毂强度数值分析[J].电气技术,2009,(8)：75-79
[28]李华明.基于有限元法的风力发电机组塔架优化设计与分析[D].乌鲁木齐：新疆农业大学,2004
[29]李德源,叶枝全,陈严,包能胜.风力机玻璃钢叶片疲劳寿命分析[J].太阳能学报,2004,25(5)：592-598
[30]张红磊.大型风电机组齿轮箱疲劳特性数值模拟[D].北京：华北电力大学,2010
[31]赵德钊.海上风机系统载荷仿真及轮毂的优化设计[D].哈尔滨：哈尔滨工业大学,2010
[32]MAL, LUANSY, JIANGCW, et al. A Review on the Forecasting of Wind Speed and Generated Power[J].Renewable & Sustainable Energy Reviews, 2009,13(4):915-920
[33]Landberg L. Short-term prediction of the power production from windfarms[J] Journal of Wind Engineering and Industrial Aerodynamics,1999,80:207-20.
[34]Taylor J W, Mc Sharry P E, Buizza R.Wind Power Density Forecaasting Using Ensemble Predictions and Time Series Models[J].IEEE Transactions on Energy Conversion,2009,24(3):775-782
[35]吴国旸,肖洋,翁莎莎.风电场短期风速预测探讨[J].吉林电力,2005(6)：21-24
[36]Bossanyi E A. Short-term Wind Prediction Using Kalman Filters[J].Wind Engineering,1985,9(1):1-8
[37]Barbounis T G, Theocharis J B, Alexiadis M C, et al. Long-term wind speed and power forecasting using local recurrent neural network models[J]. Transactions on Energy Conversion,2006,21(1):273-284
[38]谷国利,王维庆,张新燕,董红.风电场风速预测方法的研究[J].新能源产业,2009(6)：22-24
[39]Alexiadismc, Dokopoulosps, Sahsamanoglou H S, et al. Short-term Forec easting of Wind Speed andRelated Eleetrieal Power[J]. Solar Energy,1998, 63 (1):61-68
[40]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
[41]Negnevitsky M, Potter CW. Innovative short-term wind generation prediction techniques[C]. Proceedings of the Power Systems Conference and Exposition, 2006,60-65
[42]Landberg L. Short-term prediction of local wind conditions[J] Journal of Wind Engineering and Industrial Aerodynamics,2001,89(3/4):235-245
[43]曾鸣,沈又幸,吕俊昌.基于空间平滑法的风电场短期出力预测模型[J].现代电力,2008,25(3)：81-84
[44]Torres J L, Garcia A, Blas M D, etal. Forecast of hourly average wind speed with arma models in Navarre[J]. Solar Energy,2005,79(1):65-77
[45]Bilgili M, Sahin B, Yasar A. Application of artificial neural networks forthe wind speed prediction of target station using reference stations data [J].Renewable Energy,2007,32(14):2350-6
[46]Han S, Liu Y, Yang Y. Taboo search algorithm based ANN model for windspeed prediction[C].Proceedings of the 2nd IEEE Conference on Industrial Electronics and Applications,2007,2599-2602
[47]Mohandes MA, Rehman S, Halawani TO. A neural networks approach for wind speed prediction[J].Renewable Energy,1998,13(3):345-54
[48]李杏培.风电场风速及风电机组发电量的短期预报方法研究[D].保定：华北电力大学,2009
[49]高阳,陈华宇,欧阳群.风电场发电量预测技术研究综述[J].电网与清洁能源,2010,26(4)：7-13
[50]朴金姬.神经网络算法在风电场功率预测中的应用研究[D].北京：华北电力大学,2011
[51]罗培屿.基于空间相关性的超短期风速预测方法与应用[D].湘潭：湘潭大学,2012
[52]王颖,魏云军.风电场风速及风功率预测方法研究综述[J].陕西电力,2011,39(11)：18-21
[53]韩爽.风电场功率短期预测方法研究[D].北京：华北电力大学,2008
[54]Yongqian Liu, Jie Yan, Shuang Han, Yuhui Peng. Uncertainty analysis of wind power prediction based on quantile regression[C].The 4th IEEE Asia-Pacific Power and Energy Engineering Conference, Shanghai, China, March 27-29, 2012
[55]Pinson P, Kariniotakis G. On-line assessment of prediction risk for wind power production forecasts[J].Wind Energy,2004,7:119-132
[56]Luig A., Bofinger S., Beyer H. G. Analysis of confidence intervals for the prediction of regional wind power output[A].Proceeding of the 2001 European Wind Energy Association Conference, EWEC'01 Copenhagen (Denmark), 2001,2(6):725-728
[57]Bremnes JB.Probabilistic wind power forecasts using local quantile regression[J].Wind Energy,2004,7:47-54
[58]Lange M, Waldl H-P. Assessing the uncertainty of the wind power predictions with regard to specific weather situations[A].Proceeding of the 2001 European Wind Energy Association Conference, EWEC'01 Copenhagen (Denmark), 2001,2(6):695-698
[59]Hetzer J, Yu D C. An economic dispatch model incorporating wind power[J].IEEE Transactions on Energy Conversion,2008,23(2):603-611
[60]M. Carrion, J.M. Arroyo, A Computationally efficient mixed-integer linear for-mulation for the thermal unit commitment problem[J].IEEE Transactions on Power System,2006,21 (August (3)):1371-1378
[61]Chen C L. Simulated annealing-based optimal wind-thermal coordination scheduling[J].The Institution of Engineering and Technology,2007,1(3): 447-455
[62]Chen C L, Lee T Y, Jan R M. Optimal wind-thermal coordination dispatch in isolated power systems with large integration of wind capacity[J].Energy Conversion and Management,2006,47(18):3456-3472
[63]Lee T Y. Optimal spinning reserve for a wind-thermal power system using EIPSO[J].IEEE Transactions on Power Systems,2007,22(4):1612-1621
[64]周玮,彭昱,孙辉等.含风电场的电力系统动态经济调度[J].中国电机工程学报,2009,29(25)：13-18
[65]Piperaqkas G S, Anastasiadis A G, Hatziarqyriou N D. Stochastic PSO-based heat and power dispatch under environmental constraints incorporating CHP and wind power units[J].Electric Power Systems Research,2011,81(1): 209-218
[66]Lee J C, Lin W M, Liao G C, et al. Quantum genetic algorithm for dynamic economic dispatch with valve-point effects and including wind power system [J].Electrical Power and Energy Systems,2011,33(2):189-197
[67]Wang L F, Singh C. Tradeoff between risk and cost in economic dispatch including wind power penetration using particle swarm optimization[C]. International Conference on Power System Technology, Chongqing, China, 2006:1-7
[68]Hong Y Y, Li C T. Short-term real-power scheduling considering fuzzy factors in an autonomous system usinggenetic algorithms[J].The Institution of Engineering and Technology,2006,153(6):684-692
[69]陈海焱,陈金富,段献忠.含风电场电力系统经济调度的模糊建模及优化算法[J].电力系统自动化,2006,30(2)：22-26
[70]孙元章,吴俊,李国杰等.基于风速预测和随机规划的含风电场电力系统动态经济调度[J].中国电机工程学报,2009,29：41-47
[71]Miranda V, Hang P S. Economic dispatch model with fuzzy wind constraints and attitudes of dispatchers[J].IEEE Transactions on Power Systems,2005, 20(4):2143-2145
[72]Jabr R A, Pal B C. Intermittent wind generation in optimal power flow dispatching[J].IET Generation, Transmission and Distribution,2009,3(1): 66-74
[73]Wang J H, Shahidehpour M, Li Z Y. Security-constrained unit commitment with volatile wind power generation[J].IEEE Transactions on Power Systems, 2008,23(3):1319-1327
[74]Bouffard F, Galiana F D. Stochastic security for operations planning with significant wind power generation[J].IEEE Transactions on Power Systems, 2008,23(2):306-316
[75]Doherty R, Malley M O. A new approach to quantify reserve demand in systems with significant installed wind capacity[J].IEEE Transactions on Power Systems,2005,20(2):587-595
[76]Lange M. On the uncertainty of wind power predictions analysis of the forecast accuracy and statistical distribution of errors[J]. Solar Energy,2005,127(2):177-184
[77]Soder L. Reserve margin planning in a wind-hydro-thermal power system[J]. IEEE Transactions on Power Systems,1993,8(2):564-571
[78]Fabbri A, Roman T G S, Abbad J R. Assessment of the cost associated with wind generation prediction errors in a liberalized electricity market[J].IEEE Transactions on Power Systems,2005,20(3):1440-1446
[79]Matevosyan J, Soder L. Minimization of imbalance cost trading wind power on the short-term power market[J].IEEE Transactions on Power Systems, 2006,21(3):1396-1404
[80]Siahkali H, Vakilian M. Stochastic unit commitment of wind farms integrated in power system[J].Electric Power Systems Research,2010,80(9):1006-1017
[81]Piperaqkas G S, Anastasia is A G, Hatziarqyriou N D. Stochastic PSO-based heat and power dispatch under environmental constraints incorporating CHP and wind power units[J].Electric Power Systems Research,2011,81(1): 209-218
[82]Zhou W, Peng Y, Sun H. Optimal wind-thermal coordination dispatch based on risk reserve constraints [J]. European Transactions on Electrical Power,2011, 21(1):740-756
[83]Song Y H, Li F, Morgan R, et al. Comparison studies of genetic algorithms in power system economic dispatch[J].Power System Technology,1995,19(3): 27-33
[84]周玮,孙辉,顾宏,陈晓东.含风电场的电力系统经济调度研究综述[J].电力系统保护与控制,2011,39(24)：148-153
[85]R.G.deAlmeida, E.D.Castronuovo, J.A.Pecas Lopes. Optimum Generation Control in Wind Parks When Carrying out System Operator Requests [J]. IEEE Transaction on Power Systems,2006,21(2):718-725
[86]Carlos F. Moyano, Joao A. Pecas Lopes. An optimation approach for wind turbine commitment and dispatch in a wind park[J].Electric Power Systems Research,2009,79:71-79
[87]Liu J Z, Liu Y, Zeng D L, et al. Optimal short-term load dispatch strategy in wind farm[J].Science China Technological Sciences,2012,55:1140-1145
[88]刘伟.风电场集群优化调度研究[D].北京：北京交通大学,2011
[89]贺德馨.风工程与工业空气动力学[M].北京：国防工业出版社,2006
[90]Martin OLHansen.(肖劲松译).风力机空气动力学[M].北京：中国电力出版社,2009
[91]宁李谱,麦云飞,刘林.水平轴风力发电机主要载荷的确定方法[J].机械制造,2008(10),32-34
[92]Sutherland H J. On the fatigue analysis of wind turbines[S].SandiaNational laboratories,1999
[93]McCoy T, Malcolm D, Griffin D. An approach to the developmentof turbine loads in accordance with IEC 1400-1 and ISO 2394[J].1999, AIAA-99-0020
[94]王宏伟,邢波,骆红云.雨流计数法及其在疲劳寿命估算中的应用[J].矿山机械,2006,34(3):95-97
[95]赵少汴.抗疲劳设计[M].北京：机械工业出版社,1994
[96]Wirsehing P H, Wu Y T. A review of modern approaches to fatigue reliability analysis and design[J].Random fatigue life prediction, New York:ASME,1983
[97]Miner M A.Cumulative Damage[J].Fatigue, J.ApPI. Meeh.,1945:159-164
[98]Weibull W. Fatigue testing and analysis of results[M].London:Pergamon Press,1961
[99]Buch A, Seeger T, Vormwald M. Improvement of fatigue life prediction accuracy for various realistic loading spectra by use of correction factors[J].Fatigue,1986, (3):175-185
[100]王坤.大型汽轮机转子寿命问题研究[D].武汉：华中科技大学,2004
[101]胡俏,谢里阳,徐灏.相对Miner定理在疲劳可靠性分析中的应用[J].农业机械学报,1992,23(4)：61-64
[102]刘桐.3MW风电机组的载荷计算与轮毂强度分析[D].沈阳：沈阳工业大学,2012
[103]刘永前,韩爽,胡永生.风电场出力短期预报研究综述[J].现代电力,2007,24(5)：6-11
[104]李俊峰,施鹏飞,高虎.中国风电发展报告2010[R].海口：海南出版社,2010
[105]国家电网公司.风电功率预测系统功能规范(试行)[S]北京：国家电网公司, 2010
[106]Bernhard L, Kurt R, Bernhard E, et al. Wind power prediction in Germany recent advances and future challenges[C].European Wind Energy Conference, Athens,2006
[107]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
[108]HASSAN GForecasting short term wind farm production [EB/O],2006.06. 01, http://www.garradhassan.com
[109]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,2007,28(11)：1290-1294
[110]吕涛,唐巍,所丽.基于混沌相空间重构理论的风电场短期风速预测[J].电力系统保护与控制,2010,38(21)：113-117
[111]陶佳,张弘,朱国荣,等.基于优化相空间重构技术的风电场发电功率预测研究[J].中国电机工程学报,2011,31(28)：9-14.
[112]吕金虎,陆军安,陈士华.混沌时间序列分析及其应用[M].武汉：武汉大学出版社,2002
[113]杜俐.基于相空间重构和Elman神经网络的短期风电功率预测[D].郑州：华北水利水电学院,2012
[114]Rumelhart D E, Hinton G E, Williams R J. Learning representations by back-propagation error[J].Nature,1986,323(9):533-536
[115]Xi-Yue, Li-Sheng Yin, et al. Pridiction for Chotic Time Series Based on Discrete Volterra Neural Network[J].Lect Notes Computer SC,2006,27(9): 759-764
[116]张家树,肖先赐.混沌时间序列的Volterra自适应预测[J].物理学报,2000,49(3)：403-408
[117]田雨波.混合神经网络技术[M].北京：科学出版社,2009
[118]Pham D T, Liu X.Training of Elman networks and dynamoic system modeling[J].International Journal of Systems Science,1996,27(2):2212-2261
[119]Li Xiang, Chen Guanrong, Chen Zengqian et al. Chaotifying linear Elman networks[J].IEEE Transactions on Neural Networks,2002,13(5):1193-1199
[120]Cheng Y L, Huang J C, YangW C. Modeling word perception using the Elman network[J].Neurocomputing,2008,71(16/17/18):3150-3157
[121]潘柏崇.风电场电气系统设计技术的研究与应用[D].广州：华南理工大学,2009
[122]陈珩.电力系统稳态分析[M].第三版.北京：中国电力出版社,2007
[123]G.TaPia, A.TaPia, J.X.Ostolaza two alternative modeling approaches for the evaluation of wind farm active and reactive power performances[J].IEEE Transactions Energy Conversion,2006,21(12):909-920
[124]Bin Wu,Yongqiang Lang,Navid Zargari,Samir Kouro.风力发电系统的功率变换与控制[M].北京：机械工业出版社,2012.
[125]国家电网公司.风电调度运行管理规范[S]北京：国家电网公司,2010
[126]Kennedy J, Eberhart R C.Particle swarm optimization[C] Proceedings of IEEE International Conference on Neural Networks. Perth, Australia:IEEE,1995: 1942-1948
[127]刘建华.粒子群算法的基本理论及其改进研究[D].长沙：中南大学,2009
[128]Holland J H. Adaptation in Natual and Artificial System[M].Cambrigde::MIT Press,1975
[129]周明,孙树栋.遗传算法原理及其应用[M].北京：国防工业出版社,1999
[130]Kohonen T. Self-organizing maps [M].Third Edition. Berlin, Heidelberg: Springer,2001
[131]Han L. Initial weight selection methods for self organizing training[C].IEEE Proc. Of ICIPS'97. Beijing:International Academic Publishers,1997, 404-406
[132]闻新.MATLAB神经网络应用设计[M].北京：科学出版社,2000
[133]张立明.人工神经网络的模型及其应用[M].上海：复旦大学出版社,1994,137-141
[134]何清.模糊聚类分析理论与应用研究进展[J].模糊系统与数学,1998,12(2)：89-94
[135]高新波,谢维信.模糊聚类理论发展及应用的研究进展[J].科学通报,1999,44(21)：2241-2251
[136]Ruspini HE. A new approach to clustering[J].Inf Cont.,1969,15:22-28
[137]Zadeh LA. Similarity relations and fuzzy orderings[J].Journal of Information Science,1971,3:177-85
[138]Tamra S. Pattern classification based on fuzzy relations[J].IEEE Transactions on Systems, Man, and Cybernetics,1971,1(1):217-225
[139]谢维信,高新波,裴继红.模糊聚类理论发展及其应用[J].中国体视觉与图像分析,1999,4(2)：113-119
[140]张敏,于剑.基于划分的模糊聚类算法[J].软件学报,2004,15(6)：858-868
[141]Dunn J.C. Well-separated clusters and the optimal fuzzy partitions[J] Journal of Cybernet,1974,4:95-105
[142]Bezdek J.C. Pattern recognition with fuzzy objective function algorithms [M].New York:Plenum Press,1981
[143]Metropolis N, Rosenbluth A, Rosenbluth M, et al. Equation of state calculations by fast computing machines[J].Journal of Chemical Physics, 1953,21:1087-1092
[144]Kirkpatrick S, Celatt C D, Vecchi M P. Optimizationby simulated annealing[J]. Sciences,1983,.220:671-680
[2]中国可再生能源学会风能专业委员会.2013年风电装机容量统计[R].2014
[3]孙金龙,田海峰.风电场并网对电力系统的影响分析[J].太阳能,2008,10：30-32
[4]Landberg L, Watson SJ. Short term Prediction of local wind conditions[J]. Bounddary-Layer Meteorology,1994,70(1):171-195
[5]胡朝阳,王亮,郭强.华东电网大规模风电调度运行的研究[J].华东电力,2010,38(6)：0785-0788
[6]程道平.风电场的并网运行问题[J].上海节能,2008,10：34-37
[7]栾士岩,张焰,蒋传文,俞国勤,林一.含风电场的电力系统节能减排优化调度研究[J].华东电力,2010,38(1)：39-43
[8]邓恩思.大型风电场运行的特点及并网运行的问题[J].能源研究与利用,2010,4：8-11
[9]贵州电力试验研究院,华北电力科学研究院有限责任公司组编,雷铭主编.电力网降损节能手册[M].北京：中国电力出版社,2005：181-183
[10]IEC61400-1, Wind Turbine Generator Systems-Part 1:Safety Requirements[S]. Second edition 1999-02. Geneva:International Electrotechnical Commission, 1999
[11]IEC61400-1, Wind Turbines-Part 1:Design Requirements[S].Thirdedition 2005-08. Geneva:International Electrotechnical Commission,2005
[12]GL 2010 IV-part 1, Rules and Guidelines, Iv-Industrial Services, Guideline for the Certification of Wind Turbines[S].Edition 2010. Hamburg:Germanischer Lloyd WindEnergie GmbH,2010
[13]郭亮.风力发电机组疲劳载荷测试系统的研究与设计[D].乌鲁木齐：新疆大学,2006
[14]N. INOMATA, K. TSUCHIYA and S. YAM ADA. Measurement of stress on blade of NED03 500Kw prototype wind turbine[J].Renewable Energy,1999 (16):912-915
[15]M.Noda, R.G.J.Flay.A simulation model for wind turbine blade fatigue loads[J].Journal of Wind Engineering and Industrial Aerodynamics,1999, (83):527-540
[16]岳勇.风力发电机组机械零部件抗疲劳设计方法的研究[D].乌鲁木齐：新疆农业大学,2005
[17]韩玉清,吕路勇,邢晓坡.风轮叶片叶根疲劳强度分析方法的研究[J].能源工程,2009,(3)：33-36
[18]张峥,陈欣.风力机疲劳问题分析[J].华北水利水电学院学报,2008,29(3)：41-43
[19]张锦源.风力机可靠性的研究[D].汕头：汕头大学,2006
[20]宋聚众.1.5MW变速恒频风力发电机组气动与结构设计技术[D].汕头：汕头大学,2007
[21]孙靖宇.基于永磁同步电机的电动变桨研究[D].兰州：兰州交通大学,2010
[22]刘雄辉,李德源,吕文阁,成思源.水平轴风力机轮毂强度和疲劳寿命分析方法研究[J].太阳能学报,2012,33(5)：763-767
[23]王平,曹家勇,张执南.大型风力发电机轮毂强度的有限元分析与应用[J].华北电力,2009,37(7)：1206-1208
[24]Wang Ping, Cao Jiayong, Zhang Zhinan.Finite element analysis for strength of wheel hubs of large wind turbines[J].East China Electric Power,2009,37(7): 1206-1208
[25]孙传宗,姚兴佳,单光坤,等.MW级风力发电机轮毂强度分析[J].沈阳工业大学学报,2008,30(1)：46-49
[26]Brent L. Ellis & L. Montgomery Smith. Modeling and experimental testing of strain gauges in operational and failure modes. IEEE Transactions on instrumentation and measurement,2009,58 (7):1-2.
[27]邓良.大型风力发电机组轮毂强度数值分析[J].电气技术,2009,(8)：75-79
[28]李华明.基于有限元法的风力发电机组塔架优化设计与分析[D].乌鲁木齐：新疆农业大学,2004
[29]李德源,叶枝全,陈严,包能胜.风力机玻璃钢叶片疲劳寿命分析[J].太阳能学报,2004,25(5)：592-598
[30]张红磊.大型风电机组齿轮箱疲劳特性数值模拟[D].北京：华北电力大学,2010
[31]赵德钊.海上风机系统载荷仿真及轮毂的优化设计[D].哈尔滨：哈尔滨工业大学,2010
[32]MAL, LUANSY, JIANGCW, et al. A Review on the Forecasting of Wind Speed and Generated Power[J].Renewable & Sustainable Energy Reviews, 2009,13(4):915-920
[33]Landberg L. Short-term prediction of the power production from windfarms[J] Journal of Wind Engineering and Industrial Aerodynamics,1999,80:207-20.
[34]Taylor J W, Mc Sharry P E, Buizza R.Wind Power Density Forecaasting Using Ensemble Predictions and Time Series Models[J].IEEE Transactions on Energy Conversion,2009,24(3):775-782
[35]吴国旸,肖洋,翁莎莎.风电场短期风速预测探讨[J].吉林电力,2005(6)：21-24
[36]Bossanyi E A. Short-term Wind Prediction Using Kalman Filters[J].Wind Engineering,1985,9(1):1-8
[37]Barbounis T G, Theocharis J B, Alexiadis M C, et al. Long-term wind speed and power forecasting using local recurrent neural network models[J]. Transactions on Energy Conversion,2006,21(1):273-284
[38]谷国利,王维庆,张新燕,董红.风电场风速预测方法的研究[J].新能源产业,2009(6)：22-24
[39]Alexiadismc, Dokopoulosps, Sahsamanoglou H S, et al. Short-term Forec easting of Wind Speed andRelated Eleetrieal Power[J]. Solar Energy,1998, 63 (1):61-68
[40]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
[41]Negnevitsky M, Potter CW. Innovative short-term wind generation prediction techniques[C]. Proceedings of the Power Systems Conference and Exposition, 2006,60-65
[42]Landberg L. Short-term prediction of local wind conditions[J] Journal of Wind Engineering and Industrial Aerodynamics,2001,89(3/4):235-245
[43]曾鸣,沈又幸,吕俊昌.基于空间平滑法的风电场短期出力预测模型[J].现代电力,2008,25(3)：81-84
[44]Torres J L, Garcia A, Blas M D, etal. Forecast of hourly average wind speed with arma models in Navarre[J]. Solar Energy,2005,79(1):65-77
[45]Bilgili M, Sahin B, Yasar A. Application of artificial neural networks forthe wind speed prediction of target station using reference stations data [J].Renewable Energy,2007,32(14):2350-6
[46]Han S, Liu Y, Yang Y. Taboo search algorithm based ANN model for windspeed prediction[C].Proceedings of the 2nd IEEE Conference on Industrial Electronics and Applications,2007,2599-2602
[47]Mohandes MA, Rehman S, Halawani TO. A neural networks approach for wind speed prediction[J].Renewable Energy,1998,13(3):345-54
[48]李杏培.风电场风速及风电机组发电量的短期预报方法研究[D].保定：华北电力大学,2009
[49]高阳,陈华宇,欧阳群.风电场发电量预测技术研究综述[J].电网与清洁能源,2010,26(4)：7-13
[50]朴金姬.神经网络算法在风电场功率预测中的应用研究[D].北京：华北电力大学,2011
[51]罗培屿.基于空间相关性的超短期风速预测方法与应用[D].湘潭：湘潭大学,2012
[52]王颖,魏云军.风电场风速及风功率预测方法研究综述[J].陕西电力,2011,39(11)：18-21
[53]韩爽.风电场功率短期预测方法研究[D].北京：华北电力大学,2008
[54]Yongqian Liu, Jie Yan, Shuang Han, Yuhui Peng. Uncertainty analysis of wind power prediction based on quantile regression[C].The 4th IEEE Asia-Pacific Power and Energy Engineering Conference, Shanghai, China, March 27-29, 2012
[55]Pinson P, Kariniotakis G. On-line assessment of prediction risk for wind power production forecasts[J].Wind Energy,2004,7:119-132
[56]Luig A., Bofinger S., Beyer H. G. Analysis of confidence intervals for the prediction of regional wind power output[A].Proceeding of the 2001 European Wind Energy Association Conference, EWEC'01 Copenhagen (Denmark), 2001,2(6):725-728
[57]Bremnes JB.Probabilistic wind power forecasts using local quantile regression[J].Wind Energy,2004,7:47-54
[58]Lange M, Waldl H-P. Assessing the uncertainty of the wind power predictions with regard to specific weather situations[A].Proceeding of the 2001 European Wind Energy Association Conference, EWEC'01 Copenhagen (Denmark), 2001,2(6):695-698
[59]Hetzer J, Yu D C. An economic dispatch model incorporating wind power[J].IEEE Transactions on Energy Conversion,2008,23(2):603-611
[60]M. Carrion, J.M. Arroyo, A Computationally efficient mixed-integer linear for-mulation for the thermal unit commitment problem[J].IEEE Transactions on Power System,2006,21 (August (3)):1371-1378
[61]Chen C L. Simulated annealing-based optimal wind-thermal coordination scheduling[J].The Institution of Engineering and Technology,2007,1(3): 447-455
[62]Chen C L, Lee T Y, Jan R M. Optimal wind-thermal coordination dispatch in isolated power systems with large integration of wind capacity[J].Energy Conversion and Management,2006,47(18):3456-3472
[63]Lee T Y. Optimal spinning reserve for a wind-thermal power system using EIPSO[J].IEEE Transactions on Power Systems,2007,22(4):1612-1621
[64]周玮,彭昱,孙辉等.含风电场的电力系统动态经济调度[J].中国电机工程学报,2009,29(25)：13-18
[65]Piperaqkas G S, Anastasiadis A G, Hatziarqyriou N D. Stochastic PSO-based heat and power dispatch under environmental constraints incorporating CHP and wind power units[J].Electric Power Systems Research,2011,81(1): 209-218
[66]Lee J C, Lin W M, Liao G C, et al. Quantum genetic algorithm for dynamic economic dispatch with valve-point effects and including wind power system [J].Electrical Power and Energy Systems,2011,33(2):189-197
[67]Wang L F, Singh C. Tradeoff between risk and cost in economic dispatch including wind power penetration using particle swarm optimization[C]. International Conference on Power System Technology, Chongqing, China, 2006:1-7
[68]Hong Y Y, Li C T. Short-term real-power scheduling considering fuzzy factors in an autonomous system usinggenetic algorithms[J].The Institution of Engineering and Technology,2006,153(6):684-692
[69]陈海焱,陈金富,段献忠.含风电场电力系统经济调度的模糊建模及优化算法[J].电力系统自动化,2006,30(2)：22-26
[70]孙元章,吴俊,李国杰等.基于风速预测和随机规划的含风电场电力系统动态经济调度[J].中国电机工程学报,2009,29：41-47
[71]Miranda V, Hang P S. Economic dispatch model with fuzzy wind constraints and attitudes of dispatchers[J].IEEE Transactions on Power Systems,2005, 20(4):2143-2145
[72]Jabr R A, Pal B C. Intermittent wind generation in optimal power flow dispatching[J].IET Generation, Transmission and Distribution,2009,3(1): 66-74
[73]Wang J H, Shahidehpour M, Li Z Y. Security-constrained unit commitment with volatile wind power generation[J].IEEE Transactions on Power Systems, 2008,23(3):1319-1327
[74]Bouffard F, Galiana F D. Stochastic security for operations planning with significant wind power generation[J].IEEE Transactions on Power Systems, 2008,23(2):306-316
[75]Doherty R, Malley M O. A new approach to quantify reserve demand in systems with significant installed wind capacity[J].IEEE Transactions on Power Systems,2005,20(2):587-595
[76]Lange M. On the uncertainty of wind power predictions analysis of the forecast accuracy and statistical distribution of errors[J]. Solar Energy,2005,127(2):177-184
[77]Soder L. Reserve margin planning in a wind-hydro-thermal power system[J]. IEEE Transactions on Power Systems,1993,8(2):564-571
[78]Fabbri A, Roman T G S, Abbad J R. Assessment of the cost associated with wind generation prediction errors in a liberalized electricity market[J].IEEE Transactions on Power Systems,2005,20(3):1440-1446
[79]Matevosyan J, Soder L. Minimization of imbalance cost trading wind power on the short-term power market[J].IEEE Transactions on Power Systems, 2006,21(3):1396-1404
[80]Siahkali H, Vakilian M. Stochastic unit commitment of wind farms integrated in power system[J].Electric Power Systems Research,2010,80(9):1006-1017
[81]Piperaqkas G S, Anastasia is A G, Hatziarqyriou N D. Stochastic PSO-based heat and power dispatch under environmental constraints incorporating CHP and wind power units[J].Electric Power Systems Research,2011,81(1): 209-218
[82]Zhou W, Peng Y, Sun H. Optimal wind-thermal coordination dispatch based on risk reserve constraints [J]. European Transactions on Electrical Power,2011, 21(1):740-756
[83]Song Y H, Li F, Morgan R, et al. Comparison studies of genetic algorithms in power system economic dispatch[J].Power System Technology,1995,19(3): 27-33
[84]周玮,孙辉,顾宏,陈晓东.含风电场的电力系统经济调度研究综述[J].电力系统保护与控制,2011,39(24)：148-153
[85]R.G.deAlmeida, E.D.Castronuovo, J.A.Pecas Lopes. Optimum Generation Control in Wind Parks When Carrying out System Operator Requests [J]. IEEE Transaction on Power Systems,2006,21(2):718-725
[86]Carlos F. Moyano, Joao A. Pecas Lopes. An optimation approach for wind turbine commitment and dispatch in a wind park[J].Electric Power Systems Research,2009,79:71-79
[87]Liu J Z, Liu Y, Zeng D L, et al. Optimal short-term load dispatch strategy in wind farm[J].Science China Technological Sciences,2012,55:1140-1145
[88]刘伟.风电场集群优化调度研究[D].北京：北京交通大学,2011
[89]贺德馨.风工程与工业空气动力学[M].北京：国防工业出版社,2006
[90]Martin OLHansen.(肖劲松译).风力机空气动力学[M].北京：中国电力出版社,2009
[91]宁李谱,麦云飞,刘林.水平轴风力发电机主要载荷的确定方法[J].机械制造,2008(10),32-34
[92]Sutherland H J. On the fatigue analysis of wind turbines[S].SandiaNational laboratories,1999
[93]McCoy T, Malcolm D, Griffin D. An approach to the developmentof turbine loads in accordance with IEC 1400-1 and ISO 2394[J].1999, AIAA-99-0020
[94]王宏伟,邢波,骆红云.雨流计数法及其在疲劳寿命估算中的应用[J].矿山机械,2006,34(3):95-97
[95]赵少汴.抗疲劳设计[M].北京：机械工业出版社,1994
[96]Wirsehing P H, Wu Y T. A review of modern approaches to fatigue reliability analysis and design[J].Random fatigue life prediction, New York:ASME,1983
[97]Miner M A.Cumulative Damage[J].Fatigue, J.ApPI. Meeh.,1945:159-164
[98]Weibull W. Fatigue testing and analysis of results[M].London:Pergamon Press,1961
[99]Buch A, Seeger T, Vormwald M. Improvement of fatigue life prediction accuracy for various realistic loading spectra by use of correction factors[J].Fatigue,1986, (3):175-185
[100]王坤.大型汽轮机转子寿命问题研究[D].武汉：华中科技大学,2004
[101]胡俏,谢里阳,徐灏.相对Miner定理在疲劳可靠性分析中的应用[J].农业机械学报,1992,23(4)：61-64
[102]刘桐.3MW风电机组的载荷计算与轮毂强度分析[D].沈阳：沈阳工业大学,2012
[103]刘永前,韩爽,胡永生.风电场出力短期预报研究综述[J].现代电力,2007,24(5)：6-11
[104]李俊峰,施鹏飞,高虎.中国风电发展报告2010[R].海口：海南出版社,2010
[105]国家电网公司.风电功率预测系统功能规范(试行)[S]北京：国家电网公司, 2010
[106]Bernhard L, Kurt R, Bernhard E, et al. Wind power prediction in Germany recent advances and future challenges[C].European Wind Energy Conference, Athens,2006
[107]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
[108]HASSAN GForecasting short term wind farm production [EB/O],2006.06. 01, http://www.garradhassan.com
[109]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,2007,28(11)：1290-1294
[110]吕涛,唐巍,所丽.基于混沌相空间重构理论的风电场短期风速预测[J].电力系统保护与控制,2010,38(21)：113-117
[111]陶佳,张弘,朱国荣,等.基于优化相空间重构技术的风电场发电功率预测研究[J].中国电机工程学报,2011,31(28)：9-14.
[112]吕金虎,陆军安,陈士华.混沌时间序列分析及其应用[M].武汉：武汉大学出版社,2002
[113]杜俐.基于相空间重构和Elman神经网络的短期风电功率预测[D].郑州：华北水利水电学院,2012
[114]Rumelhart D E, Hinton G E, Williams R J. Learning representations by back-propagation error[J].Nature,1986,323(9):533-536
[115]Xi-Yue, Li-Sheng Yin, et al. Pridiction for Chotic Time Series Based on Discrete Volterra Neural Network[J].Lect Notes Computer SC,2006,27(9): 759-764
[116]张家树,肖先赐.混沌时间序列的Volterra自适应预测[J].物理学报,2000,49(3)：403-408
[117]田雨波.混合神经网络技术[M].北京：科学出版社,2009
[118]Pham D T, Liu X.Training of Elman networks and dynamoic system modeling[J].International Journal of Systems Science,1996,27(2):2212-2261
[119]Li Xiang, Chen Guanrong, Chen Zengqian et al. Chaotifying linear Elman networks[J].IEEE Transactions on Neural Networks,2002,13(5):1193-1199
[120]Cheng Y L, Huang J C, YangW C. Modeling word perception using the Elman network[J].Neurocomputing,2008,71(16/17/18):3150-3157
[121]潘柏崇.风电场电气系统设计技术的研究与应用[D].广州：华南理工大学,2009
[122]陈珩.电力系统稳态分析[M].第三版.北京：中国电力出版社,2007
[123]G.TaPia, A.TaPia, J.X.Ostolaza two alternative modeling approaches for the evaluation of wind farm active and reactive power performances[J].IEEE Transactions Energy Conversion,2006,21(12):909-920
[124]Bin Wu,Yongqiang Lang,Navid Zargari,Samir Kouro.风力发电系统的功率变换与控制[M].北京：机械工业出版社,2012.
[125]国家电网公司.风电调度运行管理规范[S]北京：国家电网公司,2010
[126]Kennedy J, Eberhart R C.Particle swarm optimization[C] Proceedings of IEEE International Conference on Neural Networks. Perth, Australia:IEEE,1995: 1942-1948
[127]刘建华.粒子群算法的基本理论及其改进研究[D].长沙：中南大学,2009
[128]Holland J H. Adaptation in Natual and Artificial System[M].Cambrigde::MIT Press,1975
[129]周明,孙树栋.遗传算法原理及其应用[M].北京：国防工业出版社,1999
[130]Kohonen T. Self-organizing maps [M].Third Edition. Berlin, Heidelberg: Springer,2001
[131]Han L. Initial weight selection methods for self organizing training[C].IEEE Proc. Of ICIPS'97. Beijing:International Academic Publishers,1997, 404-406
[132]闻新.MATLAB神经网络应用设计[M].北京：科学出版社,2000
[133]张立明.人工神经网络的模型及其应用[M].上海：复旦大学出版社,1994,137-141
[134]何清.模糊聚类分析理论与应用研究进展[J].模糊系统与数学,1998,12(2)：89-94
[135]高新波,谢维信.模糊聚类理论发展及应用的研究进展[J].科学通报,1999,44(21)：2241-2251
[136]Ruspini HE. A new approach to clustering[J].Inf Cont.,1969,15:22-28
[137]Zadeh LA. Similarity relations and fuzzy orderings[J].Journal of Information Science,1971,3:177-85
[138]Tamra S. Pattern classification based on fuzzy relations[J].IEEE Transactions on Systems, Man, and Cybernetics,1971,1(1):217-225
[139]谢维信,高新波,裴继红.模糊聚类理论发展及其应用[J].中国体视觉与图像分析,1999,4(2)：113-119
[140]张敏,于剑.基于划分的模糊聚类算法[J].软件学报,2004,15(6)：858-868
[141]Dunn J.C. Well-separated clusters and the optimal fuzzy partitions[J] Journal of Cybernet,1974,4:95-105
[142]Bezdek J.C. Pattern recognition with fuzzy objective function algorithms [M].New York:Plenum Press,1981
[143]Metropolis N, Rosenbluth A, Rosenbluth M, et al. Equation of state calculations by fast computing machines[J].Journal of Chemical Physics, 1953,21:1087-1092
[144]Kirkpatrick S, Celatt C D, Vecchi M P. Optimizationby simulated annealing[J]. Sciences,1983,.220:671-680