风电预测技术及其运行分析
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摘要
能源与环境问题的进一步恶化,加速了可再生能源的发展。较其他新能源发电技术,风力发电技术成熟、发电成本低,且易实现大规模产业化发展,因此逐步成为能源与环境可持续发展的主力军。风速预测的准确度关系到风电系统的调度计划,风电大规模并网必将对系统安全、稳定产生影响。风速预测是应对大规模风电并网运行问题的重要手段。本文围绕风能的随机波动性及间隙性,对风速预测修正及经济调度问题进行了研究。主要包括预测分析、预测误差修正、风功率波动性分析、储能系统容量配置以及考虑风功率预测可信度的经济调度与决策分析等一系列研究内容。
(1)本文首先对风速分布特性进行了分析,在此基础上提出一种风速综合修正预测模型,该模型由风速组合预测模型与预测误差修正模型组成。其中,组合预测模型由时间序列和BP神经网络组成,且BP模型的输入量由历史数据和时间序列得到的残差值组成。分析了风速预测误差的主要成因,提出一种新的预测误差修正模型——经验正交函数(Empirical Orthogonal Function, EOF)模型,该模型可以对风速误差值进行EOF分解,提取主要变量后,再经过EOF回归模型建立误差修正模型,具有展开式收敛快、能以少数几项逼近原场状态等优点。对比单一的时间序列模型和BP神经网络模型,风速综合修正预测模型应用于短期风速预测中,能够更好地预测风速的变化趋势。
(2)风功率波动是风电并网研究的主要内容。本文将符号时间序列方法应用于风功率波动分析中,针对风功率数据的非均匀分布,提出一种自适应分区的方法,该方法可以根据数据序列分布的密集程度,实现区域的非均匀分割,以便反映数据的变化情况。以符号序列直方图为基础,通过直方图求逆实现了原始数据序列关键区域的定位。最后以某一风电场实测风功率数据验证了本文方法的有效性,为风电功率调度提供参考。
(3)采用分区拟合的思想,将风功率预测误差分区,在每个区间中应用β分布拟合后,加权求得全区域的误差分布函数。基于此建立了考虑预测误差分布的风电场储能容量的数学模型。该方法将储能容量表示为缺失容量的函数,并详细介绍了储能容量与误差累计分布函数和荷电状态的关系。最后提出一种新的系统容量缺额的评价指标,用于比较储能容量优化效果。总之,该方法可以在一定概率水平下平抑风功率预测误差带来的功率波动,同时降低对储能系统的要求。
(4)从变速风力发电机组的空气动力学特性出发,对风功率概率统计特性进行了分析,应用非线性变换技术实现了风功率均值与协方差的快速计算。基于此本文对含风电系统的线路负载率进行了分析,算例通过对风速概率分布进行重复随机抽样,计算相应的风电场功率输出值,接着采用固有潮流计算方法对系统各状态量进行分析,最终可通过概率统计方法对各状态量的概率进行描述,验证了该方法的准确性和实用性。结论表明：该方法计算方便,也为进一步对风电并网稳定运行及对系统的影响分析提供了基础。
(5)为最大限度的捕获风能,最大功率跟踪研究一直是风电技术行业关注的热点问题。本文从变速风力发电机组空气动力学模型入手,在最大功率跟踪算法——爬山搜索法的分析基础之上,引入模拟退火算法中概率函数的定义式,用于定义自适应步长,从而使跟踪开始时具有较好的动态特性和在跟踪结束时具有较好的稳态特性。在Matlab环境下,建立了变速风力发电系统模型,仿真分析了风速变化对输出功率的影响,改进的跟踪算法能够使输出功率最终稳定在新的工作点上,且具有较好的稳态特性和动态特性。
(6)含风电系统经济调度的研究,可为大规模风电并网运行提供相关对策。本文针对风电出力预测水平的局限性,首先建立了风功率预测可信度模型,完善了风功率预测误差模型,基于此分析了考虑预测可信度的储能成本模型,结合火电机组出力成本模型完成了含风电系统的经济调度模型。模型考虑了系统失负荷、弃风概率约束及输电线路安全约束；为减小储能成本,将储能容量表示为平抑风功率预测误差的概率模型；同时在目标函数中引入储能成本调节系数,用以风功率调度水平的辅助决策。采用Monte Carlo模拟技术及模糊聚类辅助决策手段验证了本文所提方法的可行性。
The exacerbated relationship between energy and environment accelerates the development of renewable energy. With the mature generation technology, low generation cost, and convenient large-scale industrialization development, wind power will be a main part in energy and environment sustainable development. The accuracy of wind speed forecasting is related to the wind power scheduling. When large-scale wind power connected into the grid, it also affects the security and stability of the grid. Wind speed forecasting is the correct mean to solve the problem. This paper based on random fluctuation and intermittency of wind discuses the wind speed forecasting correction and economic scheduling. The main contents include that wind speed forecasting, prediction bias correcting, wind power fluctuation, energy storage systems, economic dispatch and auxiliary decision, and so on.
(1) This paper applies time series model and Back Propagation (BP) neural network model to predict wind speed. Finally, a combination model of time series and BP neural network is proposed. In the combination model, the inputs of BP neural network are made up of historical data and residual errors calculated by time series model. The main causes of speed prediction error is analysed in this paper. A new wind speed forecasting bias correction method on Empirical Orthogonal Function (EOF) is proposed. Wind speed prediction error can be decomposed by EOF, by which the main components of error are got. Then, bias correction mode can be built by regression analysis. The model can be more accurately in the short-time wind speed forecasting. And then shows an actual example.
(2) The fluctuation of wind power is the main researches in recently, and it is the key problem in wind power integration. The symbolic time series analysis method is applied in wind power fluctuation analysis in the paper. Due to the non-uniform distribution of engineering data, an adaptive partition method is proposed, according to the intensity of data sequence distribution, which can achieve non-uniform segmentation, and make the area sensitive to data. Based on the symbol sequence histograms, the key location of original data is caught by inversion of symbol sequence histograms. Finally, the validity of this method is verified by a wind farm wind power measured data, and it can provide a reference for wind power dispatching.
(3) It is not accurate that normal distribution and Laplace distribution are used to fit the wind power prediction error. The partition fitting method is put forward in this paper.(3distribution is used to fit in every section of prediction error. Error distribution function in the whole region is got by weighted summation. The mathematical model of energy storage systems capacity considered the prediction error is established. The energy storage capacity is expressed as the function of unserved energy, and detail relationships about the energy storage capacity, error cumulative distribution function and charge state are introduced. Finally, a new index of energy shortage is proposed. As a consequence, the proposed method permits the sizing of energy storage systems as a function of the desired remaining forecast uncertainty, reducing simultaneously power and energy capacity.
(4) As the development of wind power, grid-connected wind power becomes more important. Based on taking a random duplicate sample for wind speed probability distributions, calculate the corresponding wind farm output power in the paper. Then, analyzes system state variables by applying the former power flow method. Lastly, the probability descriptions of variables are obtained by probability and statistics methods. The WSCC-3-9case are shown in the paper aimed at verifying the accuracy and utility of the mentioned method. The conclusions are that the method is convenient, and provides the basis for further analysis of wind power.
(5) Based on the aerodynamic model of variable speed wind turbine and hill-climbing search this is one of Maximum Power Point Tracking (MPPT) algorithms, definition formula of probability function used in simulated annealing algorithm is referenced in the paper. It is used in defined adaptive step. The purposes are that dynamic characteristics is fine beginning of searching stage, and steady state characteristics is stratifying at the end of searching stage. The model of variable speed wind turbine and the effects of output power when wind speed changed are realized in Matlab. The conclusions are that improved algorithm can make output power stabilized in new state, dynamic characteristics and steady state characteristics are stratifying.
(6) It provides an efficient solution for wind power grid-connected operation, that study of economic dispatch. Duo to the limitation of wind power forecasting, the model of wind power prediction credibility is built, then, wind power forecasting error model is analyzed by partition fitting method. Based on those models, the model of storage energy cost considering forecasting credibility is discussed. Combining with thermal units cost, the economic dispatch model of power system containing wind farm is completed. The model considers the probability of loss of load and abandoned wind. And, in order to reduce storage energy cost is showed as the probability of wind power forecasting error. Simultaneously, the storage energy cost adjustment factor is added into the objective function, which is used to auxiliary decide scheduling wind power. The feasibility of the proposed method is verified by typical10-machine system, Monte Carlo simulation technology and fuzzy clustering is used.
(1)本文首先对风速分布特性进行了分析,在此基础上提出一种风速综合修正预测模型,该模型由风速组合预测模型与预测误差修正模型组成。其中,组合预测模型由时间序列和BP神经网络组成,且BP模型的输入量由历史数据和时间序列得到的残差值组成。分析了风速预测误差的主要成因,提出一种新的预测误差修正模型——经验正交函数(Empirical Orthogonal Function, EOF)模型,该模型可以对风速误差值进行EOF分解,提取主要变量后,再经过EOF回归模型建立误差修正模型,具有展开式收敛快、能以少数几项逼近原场状态等优点。对比单一的时间序列模型和BP神经网络模型,风速综合修正预测模型应用于短期风速预测中,能够更好地预测风速的变化趋势。
(2)风功率波动是风电并网研究的主要内容。本文将符号时间序列方法应用于风功率波动分析中,针对风功率数据的非均匀分布,提出一种自适应分区的方法,该方法可以根据数据序列分布的密集程度,实现区域的非均匀分割,以便反映数据的变化情况。以符号序列直方图为基础,通过直方图求逆实现了原始数据序列关键区域的定位。最后以某一风电场实测风功率数据验证了本文方法的有效性,为风电功率调度提供参考。
(3)采用分区拟合的思想,将风功率预测误差分区,在每个区间中应用β分布拟合后,加权求得全区域的误差分布函数。基于此建立了考虑预测误差分布的风电场储能容量的数学模型。该方法将储能容量表示为缺失容量的函数,并详细介绍了储能容量与误差累计分布函数和荷电状态的关系。最后提出一种新的系统容量缺额的评价指标,用于比较储能容量优化效果。总之,该方法可以在一定概率水平下平抑风功率预测误差带来的功率波动,同时降低对储能系统的要求。
(4)从变速风力发电机组的空气动力学特性出发,对风功率概率统计特性进行了分析,应用非线性变换技术实现了风功率均值与协方差的快速计算。基于此本文对含风电系统的线路负载率进行了分析,算例通过对风速概率分布进行重复随机抽样,计算相应的风电场功率输出值,接着采用固有潮流计算方法对系统各状态量进行分析,最终可通过概率统计方法对各状态量的概率进行描述,验证了该方法的准确性和实用性。结论表明：该方法计算方便,也为进一步对风电并网稳定运行及对系统的影响分析提供了基础。
(5)为最大限度的捕获风能,最大功率跟踪研究一直是风电技术行业关注的热点问题。本文从变速风力发电机组空气动力学模型入手,在最大功率跟踪算法——爬山搜索法的分析基础之上,引入模拟退火算法中概率函数的定义式,用于定义自适应步长,从而使跟踪开始时具有较好的动态特性和在跟踪结束时具有较好的稳态特性。在Matlab环境下,建立了变速风力发电系统模型,仿真分析了风速变化对输出功率的影响,改进的跟踪算法能够使输出功率最终稳定在新的工作点上,且具有较好的稳态特性和动态特性。
(6)含风电系统经济调度的研究,可为大规模风电并网运行提供相关对策。本文针对风电出力预测水平的局限性,首先建立了风功率预测可信度模型,完善了风功率预测误差模型,基于此分析了考虑预测可信度的储能成本模型,结合火电机组出力成本模型完成了含风电系统的经济调度模型。模型考虑了系统失负荷、弃风概率约束及输电线路安全约束；为减小储能成本,将储能容量表示为平抑风功率预测误差的概率模型；同时在目标函数中引入储能成本调节系数,用以风功率调度水平的辅助决策。采用Monte Carlo模拟技术及模糊聚类辅助决策手段验证了本文所提方法的可行性。
The exacerbated relationship between energy and environment accelerates the development of renewable energy. With the mature generation technology, low generation cost, and convenient large-scale industrialization development, wind power will be a main part in energy and environment sustainable development. The accuracy of wind speed forecasting is related to the wind power scheduling. When large-scale wind power connected into the grid, it also affects the security and stability of the grid. Wind speed forecasting is the correct mean to solve the problem. This paper based on random fluctuation and intermittency of wind discuses the wind speed forecasting correction and economic scheduling. The main contents include that wind speed forecasting, prediction bias correcting, wind power fluctuation, energy storage systems, economic dispatch and auxiliary decision, and so on.
(1) This paper applies time series model and Back Propagation (BP) neural network model to predict wind speed. Finally, a combination model of time series and BP neural network is proposed. In the combination model, the inputs of BP neural network are made up of historical data and residual errors calculated by time series model. The main causes of speed prediction error is analysed in this paper. A new wind speed forecasting bias correction method on Empirical Orthogonal Function (EOF) is proposed. Wind speed prediction error can be decomposed by EOF, by which the main components of error are got. Then, bias correction mode can be built by regression analysis. The model can be more accurately in the short-time wind speed forecasting. And then shows an actual example.
(2) The fluctuation of wind power is the main researches in recently, and it is the key problem in wind power integration. The symbolic time series analysis method is applied in wind power fluctuation analysis in the paper. Due to the non-uniform distribution of engineering data, an adaptive partition method is proposed, according to the intensity of data sequence distribution, which can achieve non-uniform segmentation, and make the area sensitive to data. Based on the symbol sequence histograms, the key location of original data is caught by inversion of symbol sequence histograms. Finally, the validity of this method is verified by a wind farm wind power measured data, and it can provide a reference for wind power dispatching.
(3) It is not accurate that normal distribution and Laplace distribution are used to fit the wind power prediction error. The partition fitting method is put forward in this paper.(3distribution is used to fit in every section of prediction error. Error distribution function in the whole region is got by weighted summation. The mathematical model of energy storage systems capacity considered the prediction error is established. The energy storage capacity is expressed as the function of unserved energy, and detail relationships about the energy storage capacity, error cumulative distribution function and charge state are introduced. Finally, a new index of energy shortage is proposed. As a consequence, the proposed method permits the sizing of energy storage systems as a function of the desired remaining forecast uncertainty, reducing simultaneously power and energy capacity.
(4) As the development of wind power, grid-connected wind power becomes more important. Based on taking a random duplicate sample for wind speed probability distributions, calculate the corresponding wind farm output power in the paper. Then, analyzes system state variables by applying the former power flow method. Lastly, the probability descriptions of variables are obtained by probability and statistics methods. The WSCC-3-9case are shown in the paper aimed at verifying the accuracy and utility of the mentioned method. The conclusions are that the method is convenient, and provides the basis for further analysis of wind power.
(5) Based on the aerodynamic model of variable speed wind turbine and hill-climbing search this is one of Maximum Power Point Tracking (MPPT) algorithms, definition formula of probability function used in simulated annealing algorithm is referenced in the paper. It is used in defined adaptive step. The purposes are that dynamic characteristics is fine beginning of searching stage, and steady state characteristics is stratifying at the end of searching stage. The model of variable speed wind turbine and the effects of output power when wind speed changed are realized in Matlab. The conclusions are that improved algorithm can make output power stabilized in new state, dynamic characteristics and steady state characteristics are stratifying.
(6) It provides an efficient solution for wind power grid-connected operation, that study of economic dispatch. Duo to the limitation of wind power forecasting, the model of wind power prediction credibility is built, then, wind power forecasting error model is analyzed by partition fitting method. Based on those models, the model of storage energy cost considering forecasting credibility is discussed. Combining with thermal units cost, the economic dispatch model of power system containing wind farm is completed. The model considers the probability of loss of load and abandoned wind. And, in order to reduce storage energy cost is showed as the probability of wind power forecasting error. Simultaneously, the storage energy cost adjustment factor is added into the objective function, which is used to auxiliary decide scheduling wind power. The feasibility of the proposed method is verified by typical10-machine system, Monte Carlo simulation technology and fuzzy clustering is used.
引文
[1]陈强,宋朝霞.我国未来新能源和可再生能源发展前景[J].能源科学进展,2006,2(2)：4-9.
[2]http://www.21cbh.com/HTML/2012-2-7/yNNDE3XzM5OTkyNg.html
[3]http://news.bjx.com.cn/html/20120802/377614.shtml
[4]http://life.21cn.com/b/bk/living/2012/11/15/13671307.shtml
[5]崔杨.大规模风电场群联网的源网协调性研究[D].华北电力大学,2011.
[6]http://www.newenergy.org.cn/html/00412/20041609.html
[7]《2011年中国风电装机容量统计表》.中国可再生能源学会风能专业委员会,2012.3.
[8]http://www.sgeri.sgcc.com.cn/zxdt/mtgz/02/115271.shtml
[9]http://news.hexun.com/2012-11-09/147798192.html
[10]《2012年上半年全国风电建设成果统计报告》.国家水电水利规划设计总院与国家风电信息管理中心,2012.10.
[11]栗然,王粤,曹磊.基于变换的风电场短期风速预测模型[J].现代电力,2008,25(4)：35-39.
[12]孙元章,吴俊,李国杰,何剑.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(4)：4]-47.
[13]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)：71-81.
[14]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.
[15]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)：27-29.
[16]潘迪夫,刘辉,李燕飞.风电场风速短期多步预测改进算法[J].中国电机工程学报,2008,28(26)：87-91.
[17]祝贺,徐建源.风电场GM-WEIBULL风速分布组合模型出力预测[J].华东电力,2008,36(11)：144-146.
[18]丁明,吴伟,吴红斌,茆美琴,张颖媛.风速概率分布参数预测及应用[J].电网技术,2008,32(14)：10-14.
[19]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,2005,252(11)：1-5.
[20]杨琦,张建华,王向峰,等.基于小波-神经网络的风速及风力发电量预测[J].电网技术,2009,22(17)：10-14.
[21]邵璠.风电场短期风速预测研究[D].新疆大学,2008.
[22]王燕.应用时间序列分析[J].北京：中国人民大学出版社.2008.
[23]George E.P.Box, Gwilym M.Jenkins, Gregory C.Reinsel著,顾岚等译.时间序列分析预测与控制[M].北京：中国统计出版社,2003,7.
[24]Keogh E., Lin J., Ada Waichee Fu. Finding Unusual Medical Time-Series Subsequences: Algorithms and Applications [J]. Information Technology in Biomedicine,2006.10(3): 429-439.
[25]Jaeseung Jeong, Gore J.C., Peterson B.S. A Method for Determinism in Short Time Series and Its Application to Stationary EEG[J]. Biomedical Engineering,2002,49(11):1374-1379.
[26]Mirikitani D.T., NiKOlaev N. Recursive Bayesian Recurrent Neural Networks for Time-Series Modeling[J]. IEEE Transactions on Neural Networks,2010,21(02):262-274.
[27]Tong H. Threshold Models in Nonlinear Time Series Analysis. New York:Springer-Verlag, 1983.
[28]Haggan V., Ozaki T. Modeling Nonlinear Vibrations Using an Amplitude-dependent Auto-regressive Time Series Model[J]. Biometrika,1981,68:189-196.
[29]Subba Rao T. On the Theory of Bilinear Time Series Models[J]. Journal of the Royal Statistical Society, Series B,1981,43:244-255.
[30]Zhang G..P. Time Series Forecasting Using A Hybrid ARIMA and Neural Network Model[J]. Neurocomputing,2003,50(1):157-175.
[31]Shanming S.,Li X., Bao L. Application of Artificial Neural Networks to the Nonlinear Combination of Forecasts[J]. Expert Systems,1996,13(3):195-201.
[32]Khotanzad A., Reza Afkhami-Rohani, Lu T., et al. ANNSTLF-A Neural-networks-based Electric Load Forecasting System[J]. IEEE Trans.on neural networks,1997,8(4):835-845.
[33]赵黎明,吴文清,刘嘉焜.基于小波分析的游客流量神经网络预测研究[J].系统工程学报,2006,21(2)：221-224.
[34]Bates J M, Granger C. The Combination of Forecast[J].Operation Research Quarterly,1969, 20:451-468.
[35]MEHMET B, BESIR S, ABDULKADIR Y. 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.
[36]SFETSOS. ANovel Approach for the Forecasting of Mean Hourly Wind Speed Time Series[J]. Renewable Energy,2002,27(2):163-174.
[37]DAMOUSIS I G, ALEXIADIS M C, THEOCHARIS J B, et al. A Fuzzy Model for Wind Speed Prediction and Power Generation in Wind Parks Using Spatial Correlarion[J]. Energy Conversion,2004,19(2):352-361.
[38]黄元生,邓佳佳,苑珍珍.基于ARMA误差修正和自适应粒子群优化的SVM短期负荷预测[J].电力系统保护与控制,2011,39(14)：26-32.
[39]周明,严正,倪以信,等.含误差预测校正的ARIMA电价预测新方法[J].中国电机工程学报,2004,24(12)：63-68.
[40]The State-of-the-art in Short-term Prediction of Wind Power:A Literature Review [EB/OL]. [2010-08-10]. http://anemos. cma.fr.
[41]徐曼,乔颖,鲁宗相.短期风电功率预测误差综合评价方法[J].电力系统自动化,2011,35(12)：21-26.
[42]黄元生,邓佳佳,苑珍珍.基于ARMA误差修正和自适应粒子群优化的SVM短期负荷预测[J].电力系统保护与控制,2011,39(14)：26-32.
[43]Zhong, M.S.. Vector Error-Correction Models in a Consumer Packaged Goods Category Forecasting Decision Support System[J]. JOURNAL OF COMPUTER INFORMATION SYSTEMS,2005,46(1):25-34.
[44]Yao, Y. An Innovative ai-conditioning Load Forecasting Model Based on RBF Neural Network and Combined Residual Error Correction [J]. INTERNATIONAL JOURNAL OF REFRIGERATION-REVUE INTERNATIONALE DU FROID,2006,29(4):528-538.
[45]Broersen, P.. Error Correction of Rainfall-runoff Models with the ARMAsel Program[J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,2007,56(6): 2212-2219.
[46]吴栋梁,王扬,郭创新,等.基于改进GMDH网络的风电场短期风速预测[J].电力系统保护与控制,2011,39(2)：88-93.
[47]孟洋洋,卢继平,工坚,等.基于Volterra自适应滤波器的风电功率混沌预测[J].电力系统保护与控制,2012,40(4)：90-95.
[48]周封,金丽斯,王丙全,等.基于高阶Markov链模型的风电功率预测性能分析[J].电力系统保护与控制,2012,40(6)：6-10.
[49]洪芦诚,石立宝,姚良忠,等.计及风电场发电功率不确定性的电力系统模糊潮流[J].电工技术学报,2010,25(8)：116-130.
[50]崔杨,穆钢,刘玉,等.风电功率波动的时空分布特性[J].电网技术,201],35(2)：110-114.
[51]于大洋,韩学山,梁军,等.基于NASA地球观测数据库的区域风电功率波动特性分析[J].电力系统自动化,2011,35(5)：77-81.
[52]Banakar H, Luo C, Ooi B K. Impacts of Wind Power Minute-to-minute Variations on Power System Operation[J]. IEEE Trans.on Power Systems,2008,23(1):150-160.
[53]Tentzerakis S, Papathanassiou S. An Investigation of the Harmonic Emissions of Wind Turbines[J]. IEEE Trans. on Energy Conversion,2007,22(1):150-158.
[54]肖创英,汪宁勃,丁坤,等.甘肃酒泉风电功率调节方式的研究[J].中国电机工程学报,2010,30(10)：1-7.
[55]Takata G, Katayama N, Miyaku M. Study on Power Fluctuation Characteristics of Wind Energy Converters with Fluctuating Turbine Torque[J]. Denki Gakkai Ronbunshi,2004, 124-B(10):1231-1239.
[56]Poul S, Nicolaos A C. Power Fluctuations From Large Wind Farms[J]. IEEE Trans on Power Systems,2007,22(3):958-965.
[57]王承煦,张源.风力发电[M].北京：中国电力出版社,2003：16-22,56-57,79-81.
[58]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].中国电机工程学报,2005,25(5)：19-24.
[59]侯佑华,房大中,齐军,等.大规模风电入网的有功功率波动特性分析及发电计划仿真[J].电网技术,2010,34(5)：60-66.
[60]李智,韩学山,杨明,等.基于分位点回归的风功率波动区间分析[J].电力系统自动化,2011,35(3)：83-87.
[61]曹娜,于群,戴慧珠.风速波动时风电场动态特性分析[J].太阳能学报,2009,30(4)：497-501.
[62]Nan Xiaoqiang, Li Quanzhan, Yu Junxiang, et al. Wind Speed Forecasting Based on Combination Forecasting Model [J]. In:2010 International Conference of Information Science and Management, Vol2. Xi'an, China,2010:185-189.
[63]M. C. Alexiadis, P. S. Dokopoulos, H. S. Sahsamanoglou. Wind Speed and Power Forecasting based on Spatial Models[J]. IEEE Transactions on Energy Conversion,1999,14(3):836-842.
[64]CAISO. Integration of Renewable Resources. www. caiso.com.2007.11.
[65]刘斌,周京阳,周海明,等.一种改进的风电功率预测误差分布模型[J].华东电力,2012,40(2)：0286-0290.
[66]葛炬,王飞,张粒子.含风电场电力系统旋转备用获取模型[J].电力系统自动化,2010,34(6)：32-36.
[67]林卫星,文劲宇,艾小猛,等.风电功率波动特性的概率分布研究[J].中国电机工程学报,2012,32(1)：38-46.
[68]陈昊,张建忠,王玉荣.基于SV模型的风速时间序列峰度分析[J].中国电力,2011,44(1)：90-93.
[69]Abbey C., Strunz K., Joos G. A Knowledge-Based Approach for Control of Two-Level Energy Storage for Wind Energy Systems[J]. IEEE Transactions on Energy Conversion,2009,24(2): 539-547.
[70]Yao D.L., Choi S.S., Tseng K.J., et al. A Statistical Approach to the Design of a Dispatchable Wind Power-Battery Energy Storage System[J]. IEEE Transactions on Energy Conversion, 2009,24(4):916-925.
[71]Brekken T.K.A., Yokochi A., von Jouanne A. et al. Optimal Energy Storage Sizing and Control for Wind Power Applications[J]. IEEE Transactions on Sustainable Energy,2011,2(1):69-77.
[72]Taguchi A, Imayoshi T, Nagafuchi T, et al. A Study of SMES Control Logic for Power System Stabilization[J]. IEEE Transactions on Applied Superconductivity,2007,17(2):2343-2346.
[73]Dechanupaprittha S, Hongesombut K, Watanabe M, et al. Stabilization of Tie-line Power Flow by Robust SMES Controller for Interconnected Power System with Wind Farms[J]. IEEE Transactions on Applied Superconductivity,2007,17(2):2365-2368.
[74]梁亮,李建林,惠东.大型风电场用储能装置容量的优化配置[J].高电压技术,2011,37(4)：930-936.
[75]Anstine L.T., Burke R.E., Casey J.E., et al. Application of Probability Methods to the Determination of Spinning Reserve Requirements for the Pennsylvania-New Jersey-Maryland Interconnection[J]. IEEE Transactions on Power Apparatus and Systems,1963,82(68): 726-735.
[76]Lee S.T., Yamayee Z.A. Load-Following and Spinning-Reserve Penalties for Intermittent Generation[J]. IEEE Transactions on Power Apparatus and Systems,1981,100(3):1203-1211.
[77]Ortega-Vazquez M. A., Kirschen D. S. Optimizing the Spinning Reserve Requirements Using a Cost/Benefit Analysis[J]. IEEE Transactions on Power Systems,2007,22(1):24-33.
[78]Ortega-Vazquez M.A., Kirschen D.S. Estimating the Spinning Reserve Requirements in Systems With Significant Wind Power Generation Penetration [J]. IEEE Transactions on Power Systems,2009,24(1):114-124.
[79]Da Silva A.M.L.L., Sales W.S., da Fonseca Manso L.A., et al. Long-Term Probabilistic Evaluation of Operating Reserve Requirements With Renewable Sources[J]. IEEE Transactions on Power Systems,2010,25, (1):106-116.
[80]Hartmann B., Dan A. Cooperation of a Grid-Connected Wind Farm and an Energy Storage Unit—Demonstration of a Simulation Tool[J]. IEEE Transactions on Sustainable Energy, 2012,3(1):49-56.
[81]文艺,张步涵,毛承雄,等.风电场中储能系统的功率和容量优化配置[J].湖北工业大学学报,2012,27(1)：18-21.
[82]卢继平,朱三立,韩涛,等.风电场储能容量合理取值范围分析[J].重庆大学学报,2010,33(8)：46-51.
[83]倪受元.风力发电用的发电机及风力发电系统[J].太阳能学报,2000(4)：12-17.
[84]石立宝,戴世强,徐政,等.大规模双馈型风电场并网的系统暂态稳定仿真[J].电系统及其自动化学报,2009,21(4)：1-7.
[85]Feijoo A, Cidras J. Modeling of Wind Farms in the Load Flow Analysis[J]. IEEE Trans on Power Systems,2000,15(1):110-115.
[86]吴俊玲,周双喜,孙建锋,等.并网风力发电场的最大注入功率分析[J].电网技术,2004,28(20)：28-32.
[87]A. M. LeitedaSilva, V. L. Arienti, R. N. Allan. Probabilistic Load Flow Considering Dependence Between Input Nodal Powers[J]. IEEE Trans on Power Systems,1984,103(6): 1524-1530.
[88]P. Jorgensen, J.O.Tande. Probabilistic Load Flow Calculation Using Monte Carlo Techniques for Distribution Network with Wind Turbines[J]. IEEE Trans on Power Systems,1998,13(2): 1146-1151.
[89]吴义纯,丁明,张立军.含风电场的电力系统潮流计算[J].中国电机工程学报,2005,25(4)：36-39.
[90]王海超,周双喜,鲁宗相,等.含风电场的电力系统潮流计算的联合迭代方法及应用[J].电网技术,2005,25(18)：59-62.
[91]Moor G D, Beykes H J. Maximum Power Pointtrackers for Wind Turbines[C]. Proceedings of IEEE 35th Annual Power Electronics Specialists Conference, Aachen Germany,2004.
[92]Li Hu, Shi KL, MCLARENP. Neural Network Based Sensor Less Maximum Wind Energy Capture with Compensated Power Coefficient J]. In:Conference Record of the 39th IEEE Industry Applications Conference, Vol4. Seattle(WA, USA),2004:2600-2608.
[93]王栋.风力发电机的最大功率跟踪控制方法研究[D].内蒙古：内蒙古大学,2009.
[94]胡家兵,贺益康,刘其辉.基于最佳功率给定的最大风能追踪控制策略[J].电力系统自动化,2005,29(24)：32-38.
[95]蒋禹,高雪松.双馈型变速恒频风力发电系统最大风能追踪控制研究[J].电网技术,2008,32(S2)：260-263.
[96]Mutschler P, Meinhardt M. Competitive Hysteresis Controllers-a Control Concept for Inverters Having Oscillating DC and AC Side State Variables[J]. IEE Proceedings on Electric Power Application,1998,145(6):569-576.
[97]SIMOES MG, BOSE BK, SPIEGEL RJ. Fuzzy Logic Based Intelligent Control of a Variable Speed Cage Machine Wind Generation System [J]. IEEE Ttans on Power Electronics,1997, 12(1):87-95.
[98]R.G. De Almeida, J.A.P. Lopes. Participation of Doubly Fed Induction Wind Generators in System Frequency Regulation[J]. IEEE Transactions on Power Systems,2007,22(3):944-950.
[99]Liang R H, Liao Jianhao. A Fuzzy-optimization Approach for Generation Scheduling with Wind and Solar Energy Systems[J]. IEEE Transactions on Power Systems,2007,22(4): 1665-1674.
[100]Hetzer J, Yu D C, Bhattarai K. An Economic Dispatch Model Incorporating Wind Power[J]. IEEE Transactions on Energy Conversion,2008,23(2):603-611.
[101]Tapia G., Tapia A., Ostolaza J.X. Proportional-Integral Regulator-Based Approach to Wind Farm Reactive Power Management for Secondary Voltage Control[J]. IEEE Transactions on Energy Conversion,2007,22(2):488-498.
[102]Ullah N.R., Thiringer T., Karlsson D. Temporary Primary Frequency Control Support by Variable Speed Wind Turbines Potential and Applications[J]. IEEE Transactions on Power Systems,2008,23(2):601-612.
[103]Gonz a lez J, Ruiz R. Stochastic Joint Optimization of Wind Generation and Pumped-storage Units in An Electricity Market[J]. IEEE Transactions on Power Systems,2008, 23(2):460-468.
[104]袁铁江,晁勤,吐尔逊·伊不拉音,等.大规模风电并网电力系统动态清洁经济优化调度的建模[J].中国电机工程学报,2010,30(31)：7-13.
[105]袁铁江,晁勤,李义岩,等.基于风电极限穿透功率的经济调度优化模型研究[J].电力系统保护与控制,2011,39(1)：15-22.
[106]Tsung-Ying Lee. Optimal Spinning Reserve for a Wind-Thermal Power System Using EIPSO[J]. IEEE Transactions on Power Systems,2007,22(4):1612-1621.
[107]Chun-Lung Chen. Optimal Wind-Thermal Generating Unit Commitment[J]. IEEE Transactions on Energy Conversion,2008,23(1):273-280.
[108]张保会,李光辉,王进,等.风电接入电力系统故障电流的影响因素分析及对继电保护的影响[J].电力自动化设备,2012,32(2)：1-8.
[109]陈道君,龚庆武,张茂林,等.考虑能源环境效益的含风电场多目标优化调度[J].中国电机工程学报,2011,31(13)：10-17.
[110]Liu Xian, Xu Wilsun. Economic Load Dispatch Constrained by Wind Power Availability: a Here-and-now Approach [J]. IEEE Transactions on Sustainable Energy,2010,1(1):2-9.
[111]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.
[112]Liu Xian, Xu Wilsun. Minimum Emission Dispatch Constrained by Stochastic Wind Power Availability and Cost[J]. IEEE Transactions on Power Systems,2010,25(3):1705-1713.
[113]李智,韩学山,杨明,等.计及接纳风电能力的电网调度模型[J].电力系统自动化,2010,34(19)：15-19.
[114]郑太一,冯利民,王绍然,等.一种计及电网安全约束的风电优化调度方法[J].电力系统自动化,2010,34(15)：71-74.
[115]Bates J M, Granger C W J. Combination of forecasts[J]. Operational Research Quarterly, 1969,20(4):451-468.
[116]丁明,张立军,吴义纯.基于时间序列分析的风电场风速预测模型[J].自动化设备,2005,25(8)：32-34.
[117]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].华北电力,2009.
[118]潘迪夫,刘辉,李燕飞.风电场风速短期多步预测改进算法[J].中国电机工程学报,2008,28(26)：87-91.
[119]丁明,吴伟,吴红斌,等.风速概率分布参数预测及应用[J].电网技术,2008,32(14)：10-14.
[120]杨秀嫒,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,2005,252(11)：1-5.
[121]Pearson K. On Lines and Plans of Closest Fit to System of Points in Space Philos[J]. Magnetism,1902,52(6):11-15.
[122]FINNEY C E A, GREEN J B J, DAW C S. Symbolic Time-series Analysis of Engine Combustion Measurements[J]. SAE Paper No.980624.
[123]蔡觉平,李赞,宋文涛.一种混沌伪随机序列复杂度分析法[J].物理学报,2003,52(8)：1871-1876.
[124]金宁德,李伟波.非线性时间序列的符号化分析方法研究[J].动力学与控制学报,2004,2(3)：54-59.
[125]Kurths J, Schwarz U, Witt A, et al. Measures of Complexity in Signal Analysis[J]. Proc of the AIP Conference on Chaotic, Fractal, and Nonlinear Signal Processing. Woodbury, USA, 1996:33-54.
[126]Ray A. Symbolic Dynamic Analysis of Complex Systems for Anomaly Detection[J]. Signal Processing,2004,84(7):1115-1130.
[127]孟祥星,王宏.大规模风电并网条件下的电力系统调度[J].东北电力大学学报,2009,29(1)：1-7.
[128]李筠,祝勇.数据处理的Beta分布拟合法[J].仪器仪表学报,2004,25(4)：762-763.
[129]Bludszuweit, H.; Dominguez-Navarro, J.A.; Llombart, A., Statistical Analysis of Wind Power Forecast Error[J] Power Systems, IEEE Transactions on, vol.23, no.3, pp.983,991, Aug.2008.
[130]Bludszuweit, H.; Dominguez-Navarro, J.A., A Probabilistic Method for Energy Storage Sizing Based on Wind Power Forecast Uncertainty[J]Power Systems, IEEE Transactions on, vol.26, no.3, pp.1651,1658, Aug.2011.
[131]Papadopolos M, Malatestas P, Hatziagyriou N. Simulation and Analysis of Small and Medium Size Power Systems Containing Wind Turbines[J]. IEEE Trans on Power systems, 1991,6(4):1453-1458.
[132]Saad S Z, Jenkins N. Models for Predicting Flicker Induced by Large Wind Turbines[J]. IEEE Trans on Power Systems,1999,14(3):743-748.
[133]Andres E Feijoo, JosoeCidras. Modeling of Wind Farms in the Load Flow Analysis[J]. IEEE Transactions on Power System,2000,15(1):110-115.
[134]G. S. Stavrakakis, GN. Kariniotakis. A General Simulation Algorithm for the Accurate Assessment of Island Diesel-Wind Turbines System Interaction[J]. IEEE Transactions on Energy Conversion,1995,10(3):577-553.
[135]李建林,许洪华,高志刚,胡书举,朱颖.风力发电中的电力电子变流技术[M].北京：机械工业出版社,2008.
[136]闫永勤,乔明,林飞等.变速恒频风力发电系统最大风能跟踪控制的研究[J].电气技术,2006,11：14-17.
[137]南晓强,李群湛,刘航.含风电系统线路负载率的概率计算法[J].电力系统及其自动化学报,2012,24(4)：126-129.
[138]申洪,王伟胜,戴慧珠.变速恒频风力发电机组的无功功率极限[J].电网技术,2003,27(11)：60-63.
[139]陈树勇,戴慧珠,白晓民,等.风电场的发电可靠性模型及其应用[J].中国电机工程学报,2000,20(3)：26-29.
[140]Matthias Lange, Detlev Heinemann. Relating the Uncertainty of Short-term Wind Speed Predictions to Meteorological Situations with Methods from Synoptic Climatology[J]. European Wind Energy Conference&Exhibition EWEC 2003, Madrid, Spain.
[141]Matthias Lange. On the Uncertainty of Wind Power Predictions-analysis of the Forecast Accuracy and Statistical Distribution of Errors[J]. Journal of Solar Energy Engineering,2005, 127:177-184.
[142]P. Pinson, G. N. Kariniotakis. Wind Power Forecasting using Fuzzy Neural Networks Enhanced with On-line Prediction Risk Assessment[J].2003 IEEE Bologna PowerTech Conference, June 23-26, Bologna, Italy.
[143]Luig A., Bofinger S., Beyer H.G.. Analysis of Confidence Intervals for the Prediction of Regional Wind Power Output[J]. Proc.of the 2001 European Wind Energy Association Conference, EWEC'01, Copenhagen(Denmark),2001,2-6 July:725-728.
[144]Lange M., Waldl H.-P. Assessing the Uncertainty of the Wind Power Predictions with Regard to Specific Weather Situations[J]. Proc. of the 2001 European Wind Energy Association Conference, EWEC'01, Copenhagen (Denmark),2001,2-6 July:695-698.
[145]Roy S. Market Constrained Optimal Planning for Wind Energy Conversion Systems Over Multiple Installation Sites[J]. IEEE Trans. on Energy Conversion,2002,17(1):124-129.
[146]周玮,孙辉,顾宏,等.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)：148-154.
[147]韩自奋,陈启卷.考虑约束的风电调度模式[J].电力系统自动化,2010,,3(2)：89-92.
[148]王鹏,任冲,彭明侨.西北电网风电调度运行管理研究[J].电网与清洁能源,2009,25(11)：80-84.
[2]http://www.21cbh.com/HTML/2012-2-7/yNNDE3XzM5OTkyNg.html
[3]http://news.bjx.com.cn/html/20120802/377614.shtml
[4]http://life.21cn.com/b/bk/living/2012/11/15/13671307.shtml
[5]崔杨.大规模风电场群联网的源网协调性研究[D].华北电力大学,2011.
[6]http://www.newenergy.org.cn/html/00412/20041609.html
[7]《2011年中国风电装机容量统计表》.中国可再生能源学会风能专业委员会,2012.3.
[8]http://www.sgeri.sgcc.com.cn/zxdt/mtgz/02/115271.shtml
[9]http://news.hexun.com/2012-11-09/147798192.html
[10]《2012年上半年全国风电建设成果统计报告》.国家水电水利规划设计总院与国家风电信息管理中心,2012.10.
[11]栗然,王粤,曹磊.基于变换的风电场短期风速预测模型[J].现代电力,2008,25(4)：35-39.
[12]孙元章,吴俊,李国杰,何剑.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(4)：4]-47.
[13]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)：71-81.
[14]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.
[15]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)：27-29.
[16]潘迪夫,刘辉,李燕飞.风电场风速短期多步预测改进算法[J].中国电机工程学报,2008,28(26)：87-91.
[17]祝贺,徐建源.风电场GM-WEIBULL风速分布组合模型出力预测[J].华东电力,2008,36(11)：144-146.
[18]丁明,吴伟,吴红斌,茆美琴,张颖媛.风速概率分布参数预测及应用[J].电网技术,2008,32(14)：10-14.
[19]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,2005,252(11)：1-5.
[20]杨琦,张建华,王向峰,等.基于小波-神经网络的风速及风力发电量预测[J].电网技术,2009,22(17)：10-14.
[21]邵璠.风电场短期风速预测研究[D].新疆大学,2008.
[22]王燕.应用时间序列分析[J].北京：中国人民大学出版社.2008.
[23]George E.P.Box, Gwilym M.Jenkins, Gregory C.Reinsel著,顾岚等译.时间序列分析预测与控制[M].北京：中国统计出版社,2003,7.
[24]Keogh E., Lin J., Ada Waichee Fu. Finding Unusual Medical Time-Series Subsequences: Algorithms and Applications [J]. Information Technology in Biomedicine,2006.10(3): 429-439.
[25]Jaeseung Jeong, Gore J.C., Peterson B.S. A Method for Determinism in Short Time Series and Its Application to Stationary EEG[J]. Biomedical Engineering,2002,49(11):1374-1379.
[26]Mirikitani D.T., NiKOlaev N. Recursive Bayesian Recurrent Neural Networks for Time-Series Modeling[J]. IEEE Transactions on Neural Networks,2010,21(02):262-274.
[27]Tong H. Threshold Models in Nonlinear Time Series Analysis. New York:Springer-Verlag, 1983.
[28]Haggan V., Ozaki T. Modeling Nonlinear Vibrations Using an Amplitude-dependent Auto-regressive Time Series Model[J]. Biometrika,1981,68:189-196.
[29]Subba Rao T. On the Theory of Bilinear Time Series Models[J]. Journal of the Royal Statistical Society, Series B,1981,43:244-255.
[30]Zhang G..P. Time Series Forecasting Using A Hybrid ARIMA and Neural Network Model[J]. Neurocomputing,2003,50(1):157-175.
[31]Shanming S.,Li X., Bao L. Application of Artificial Neural Networks to the Nonlinear Combination of Forecasts[J]. Expert Systems,1996,13(3):195-201.
[32]Khotanzad A., Reza Afkhami-Rohani, Lu T., et al. ANNSTLF-A Neural-networks-based Electric Load Forecasting System[J]. IEEE Trans.on neural networks,1997,8(4):835-845.
[33]赵黎明,吴文清,刘嘉焜.基于小波分析的游客流量神经网络预测研究[J].系统工程学报,2006,21(2)：221-224.
[34]Bates J M, Granger C. The Combination of Forecast[J].Operation Research Quarterly,1969, 20:451-468.
[35]MEHMET B, BESIR S, ABDULKADIR Y. 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.
[36]SFETSOS. ANovel Approach for the Forecasting of Mean Hourly Wind Speed Time Series[J]. Renewable Energy,2002,27(2):163-174.
[37]DAMOUSIS I G, ALEXIADIS M C, THEOCHARIS J B, et al. A Fuzzy Model for Wind Speed Prediction and Power Generation in Wind Parks Using Spatial Correlarion[J]. Energy Conversion,2004,19(2):352-361.
[38]黄元生,邓佳佳,苑珍珍.基于ARMA误差修正和自适应粒子群优化的SVM短期负荷预测[J].电力系统保护与控制,2011,39(14)：26-32.
[39]周明,严正,倪以信,等.含误差预测校正的ARIMA电价预测新方法[J].中国电机工程学报,2004,24(12)：63-68.
[40]The State-of-the-art in Short-term Prediction of Wind Power:A Literature Review [EB/OL]. [2010-08-10]. http://anemos. cma.fr.
[41]徐曼,乔颖,鲁宗相.短期风电功率预测误差综合评价方法[J].电力系统自动化,2011,35(12)：21-26.
[42]黄元生,邓佳佳,苑珍珍.基于ARMA误差修正和自适应粒子群优化的SVM短期负荷预测[J].电力系统保护与控制,2011,39(14)：26-32.
[43]Zhong, M.S.. Vector Error-Correction Models in a Consumer Packaged Goods Category Forecasting Decision Support System[J]. JOURNAL OF COMPUTER INFORMATION SYSTEMS,2005,46(1):25-34.
[44]Yao, Y. An Innovative ai-conditioning Load Forecasting Model Based on RBF Neural Network and Combined Residual Error Correction [J]. INTERNATIONAL JOURNAL OF REFRIGERATION-REVUE INTERNATIONALE DU FROID,2006,29(4):528-538.
[45]Broersen, P.. Error Correction of Rainfall-runoff Models with the ARMAsel Program[J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,2007,56(6): 2212-2219.
[46]吴栋梁,王扬,郭创新,等.基于改进GMDH网络的风电场短期风速预测[J].电力系统保护与控制,2011,39(2)：88-93.
[47]孟洋洋,卢继平,工坚,等.基于Volterra自适应滤波器的风电功率混沌预测[J].电力系统保护与控制,2012,40(4)：90-95.
[48]周封,金丽斯,王丙全,等.基于高阶Markov链模型的风电功率预测性能分析[J].电力系统保护与控制,2012,40(6)：6-10.
[49]洪芦诚,石立宝,姚良忠,等.计及风电场发电功率不确定性的电力系统模糊潮流[J].电工技术学报,2010,25(8)：116-130.
[50]崔杨,穆钢,刘玉,等.风电功率波动的时空分布特性[J].电网技术,201],35(2)：110-114.
[51]于大洋,韩学山,梁军,等.基于NASA地球观测数据库的区域风电功率波动特性分析[J].电力系统自动化,2011,35(5)：77-81.
[52]Banakar H, Luo C, Ooi B K. Impacts of Wind Power Minute-to-minute Variations on Power System Operation[J]. IEEE Trans.on Power Systems,2008,23(1):150-160.
[53]Tentzerakis S, Papathanassiou S. An Investigation of the Harmonic Emissions of Wind Turbines[J]. IEEE Trans. on Energy Conversion,2007,22(1):150-158.
[54]肖创英,汪宁勃,丁坤,等.甘肃酒泉风电功率调节方式的研究[J].中国电机工程学报,2010,30(10)：1-7.
[55]Takata G, Katayama N, Miyaku M. Study on Power Fluctuation Characteristics of Wind Energy Converters with Fluctuating Turbine Torque[J]. Denki Gakkai Ronbunshi,2004, 124-B(10):1231-1239.
[56]Poul S, Nicolaos A C. Power Fluctuations From Large Wind Farms[J]. IEEE Trans on Power Systems,2007,22(3):958-965.
[57]王承煦,张源.风力发电[M].北京：中国电力出版社,2003：16-22,56-57,79-81.
[58]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].中国电机工程学报,2005,25(5)：19-24.
[59]侯佑华,房大中,齐军,等.大规模风电入网的有功功率波动特性分析及发电计划仿真[J].电网技术,2010,34(5)：60-66.
[60]李智,韩学山,杨明,等.基于分位点回归的风功率波动区间分析[J].电力系统自动化,2011,35(3)：83-87.
[61]曹娜,于群,戴慧珠.风速波动时风电场动态特性分析[J].太阳能学报,2009,30(4)：497-501.
[62]Nan Xiaoqiang, Li Quanzhan, Yu Junxiang, et al. Wind Speed Forecasting Based on Combination Forecasting Model [J]. In:2010 International Conference of Information Science and Management, Vol2. Xi'an, China,2010:185-189.
[63]M. C. Alexiadis, P. S. Dokopoulos, H. S. Sahsamanoglou. Wind Speed and Power Forecasting based on Spatial Models[J]. IEEE Transactions on Energy Conversion,1999,14(3):836-842.
[64]CAISO. Integration of Renewable Resources. www. caiso.com.2007.11.
[65]刘斌,周京阳,周海明,等.一种改进的风电功率预测误差分布模型[J].华东电力,2012,40(2)：0286-0290.
[66]葛炬,王飞,张粒子.含风电场电力系统旋转备用获取模型[J].电力系统自动化,2010,34(6)：32-36.
[67]林卫星,文劲宇,艾小猛,等.风电功率波动特性的概率分布研究[J].中国电机工程学报,2012,32(1)：38-46.
[68]陈昊,张建忠,王玉荣.基于SV模型的风速时间序列峰度分析[J].中国电力,2011,44(1)：90-93.
[69]Abbey C., Strunz K., Joos G. A Knowledge-Based Approach for Control of Two-Level Energy Storage for Wind Energy Systems[J]. IEEE Transactions on Energy Conversion,2009,24(2): 539-547.
[70]Yao D.L., Choi S.S., Tseng K.J., et al. A Statistical Approach to the Design of a Dispatchable Wind Power-Battery Energy Storage System[J]. IEEE Transactions on Energy Conversion, 2009,24(4):916-925.
[71]Brekken T.K.A., Yokochi A., von Jouanne A. et al. Optimal Energy Storage Sizing and Control for Wind Power Applications[J]. IEEE Transactions on Sustainable Energy,2011,2(1):69-77.
[72]Taguchi A, Imayoshi T, Nagafuchi T, et al. A Study of SMES Control Logic for Power System Stabilization[J]. IEEE Transactions on Applied Superconductivity,2007,17(2):2343-2346.
[73]Dechanupaprittha S, Hongesombut K, Watanabe M, et al. Stabilization of Tie-line Power Flow by Robust SMES Controller for Interconnected Power System with Wind Farms[J]. IEEE Transactions on Applied Superconductivity,2007,17(2):2365-2368.
[74]梁亮,李建林,惠东.大型风电场用储能装置容量的优化配置[J].高电压技术,2011,37(4)：930-936.
[75]Anstine L.T., Burke R.E., Casey J.E., et al. Application of Probability Methods to the Determination of Spinning Reserve Requirements for the Pennsylvania-New Jersey-Maryland Interconnection[J]. IEEE Transactions on Power Apparatus and Systems,1963,82(68): 726-735.
[76]Lee S.T., Yamayee Z.A. Load-Following and Spinning-Reserve Penalties for Intermittent Generation[J]. IEEE Transactions on Power Apparatus and Systems,1981,100(3):1203-1211.
[77]Ortega-Vazquez M. A., Kirschen D. S. Optimizing the Spinning Reserve Requirements Using a Cost/Benefit Analysis[J]. IEEE Transactions on Power Systems,2007,22(1):24-33.
[78]Ortega-Vazquez M.A., Kirschen D.S. Estimating the Spinning Reserve Requirements in Systems With Significant Wind Power Generation Penetration [J]. IEEE Transactions on Power Systems,2009,24(1):114-124.
[79]Da Silva A.M.L.L., Sales W.S., da Fonseca Manso L.A., et al. Long-Term Probabilistic Evaluation of Operating Reserve Requirements With Renewable Sources[J]. IEEE Transactions on Power Systems,2010,25, (1):106-116.
[80]Hartmann B., Dan A. Cooperation of a Grid-Connected Wind Farm and an Energy Storage Unit—Demonstration of a Simulation Tool[J]. IEEE Transactions on Sustainable Energy, 2012,3(1):49-56.
[81]文艺,张步涵,毛承雄,等.风电场中储能系统的功率和容量优化配置[J].湖北工业大学学报,2012,27(1)：18-21.
[82]卢继平,朱三立,韩涛,等.风电场储能容量合理取值范围分析[J].重庆大学学报,2010,33(8)：46-51.
[83]倪受元.风力发电用的发电机及风力发电系统[J].太阳能学报,2000(4)：12-17.
[84]石立宝,戴世强,徐政,等.大规模双馈型风电场并网的系统暂态稳定仿真[J].电系统及其自动化学报,2009,21(4)：1-7.
[85]Feijoo A, Cidras J. Modeling of Wind Farms in the Load Flow Analysis[J]. IEEE Trans on Power Systems,2000,15(1):110-115.
[86]吴俊玲,周双喜,孙建锋,等.并网风力发电场的最大注入功率分析[J].电网技术,2004,28(20)：28-32.
[87]A. M. LeitedaSilva, V. L. Arienti, R. N. Allan. Probabilistic Load Flow Considering Dependence Between Input Nodal Powers[J]. IEEE Trans on Power Systems,1984,103(6): 1524-1530.
[88]P. Jorgensen, J.O.Tande. Probabilistic Load Flow Calculation Using Monte Carlo Techniques for Distribution Network with Wind Turbines[J]. IEEE Trans on Power Systems,1998,13(2): 1146-1151.
[89]吴义纯,丁明,张立军.含风电场的电力系统潮流计算[J].中国电机工程学报,2005,25(4)：36-39.
[90]王海超,周双喜,鲁宗相,等.含风电场的电力系统潮流计算的联合迭代方法及应用[J].电网技术,2005,25(18)：59-62.
[91]Moor G D, Beykes H J. Maximum Power Pointtrackers for Wind Turbines[C]. Proceedings of IEEE 35th Annual Power Electronics Specialists Conference, Aachen Germany,2004.
[92]Li Hu, Shi KL, MCLARENP. Neural Network Based Sensor Less Maximum Wind Energy Capture with Compensated Power Coefficient J]. In:Conference Record of the 39th IEEE Industry Applications Conference, Vol4. Seattle(WA, USA),2004:2600-2608.
[93]王栋.风力发电机的最大功率跟踪控制方法研究[D].内蒙古：内蒙古大学,2009.
[94]胡家兵,贺益康,刘其辉.基于最佳功率给定的最大风能追踪控制策略[J].电力系统自动化,2005,29(24)：32-38.
[95]蒋禹,高雪松.双馈型变速恒频风力发电系统最大风能追踪控制研究[J].电网技术,2008,32(S2)：260-263.
[96]Mutschler P, Meinhardt M. Competitive Hysteresis Controllers-a Control Concept for Inverters Having Oscillating DC and AC Side State Variables[J]. IEE Proceedings on Electric Power Application,1998,145(6):569-576.
[97]SIMOES MG, BOSE BK, SPIEGEL RJ. Fuzzy Logic Based Intelligent Control of a Variable Speed Cage Machine Wind Generation System [J]. IEEE Ttans on Power Electronics,1997, 12(1):87-95.
[98]R.G. De Almeida, J.A.P. Lopes. Participation of Doubly Fed Induction Wind Generators in System Frequency Regulation[J]. IEEE Transactions on Power Systems,2007,22(3):944-950.
[99]Liang R H, Liao Jianhao. A Fuzzy-optimization Approach for Generation Scheduling with Wind and Solar Energy Systems[J]. IEEE Transactions on Power Systems,2007,22(4): 1665-1674.
[100]Hetzer J, Yu D C, Bhattarai K. An Economic Dispatch Model Incorporating Wind Power[J]. IEEE Transactions on Energy Conversion,2008,23(2):603-611.
[101]Tapia G., Tapia A., Ostolaza J.X. Proportional-Integral Regulator-Based Approach to Wind Farm Reactive Power Management for Secondary Voltage Control[J]. IEEE Transactions on Energy Conversion,2007,22(2):488-498.
[102]Ullah N.R., Thiringer T., Karlsson D. Temporary Primary Frequency Control Support by Variable Speed Wind Turbines Potential and Applications[J]. IEEE Transactions on Power Systems,2008,23(2):601-612.
[103]Gonz a lez J, Ruiz R. Stochastic Joint Optimization of Wind Generation and Pumped-storage Units in An Electricity Market[J]. IEEE Transactions on Power Systems,2008, 23(2):460-468.
[104]袁铁江,晁勤,吐尔逊·伊不拉音,等.大规模风电并网电力系统动态清洁经济优化调度的建模[J].中国电机工程学报,2010,30(31)：7-13.
[105]袁铁江,晁勤,李义岩,等.基于风电极限穿透功率的经济调度优化模型研究[J].电力系统保护与控制,2011,39(1)：15-22.
[106]Tsung-Ying Lee. Optimal Spinning Reserve for a Wind-Thermal Power System Using EIPSO[J]. IEEE Transactions on Power Systems,2007,22(4):1612-1621.
[107]Chun-Lung Chen. Optimal Wind-Thermal Generating Unit Commitment[J]. IEEE Transactions on Energy Conversion,2008,23(1):273-280.
[108]张保会,李光辉,王进,等.风电接入电力系统故障电流的影响因素分析及对继电保护的影响[J].电力自动化设备,2012,32(2)：1-8.
[109]陈道君,龚庆武,张茂林,等.考虑能源环境效益的含风电场多目标优化调度[J].中国电机工程学报,2011,31(13)：10-17.
[110]Liu Xian, Xu Wilsun. Economic Load Dispatch Constrained by Wind Power Availability: a Here-and-now Approach [J]. IEEE Transactions on Sustainable Energy,2010,1(1):2-9.
[111]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.
[112]Liu Xian, Xu Wilsun. Minimum Emission Dispatch Constrained by Stochastic Wind Power Availability and Cost[J]. IEEE Transactions on Power Systems,2010,25(3):1705-1713.
[113]李智,韩学山,杨明,等.计及接纳风电能力的电网调度模型[J].电力系统自动化,2010,34(19)：15-19.
[114]郑太一,冯利民,王绍然,等.一种计及电网安全约束的风电优化调度方法[J].电力系统自动化,2010,34(15)：71-74.
[115]Bates J M, Granger C W J. Combination of forecasts[J]. Operational Research Quarterly, 1969,20(4):451-468.
[116]丁明,张立军,吴义纯.基于时间序列分析的风电场风速预测模型[J].自动化设备,2005,25(8)：32-34.
[117]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].华北电力,2009.
[118]潘迪夫,刘辉,李燕飞.风电场风速短期多步预测改进算法[J].中国电机工程学报,2008,28(26)：87-91.
[119]丁明,吴伟,吴红斌,等.风速概率分布参数预测及应用[J].电网技术,2008,32(14)：10-14.
[120]杨秀嫒,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,2005,252(11)：1-5.
[121]Pearson K. On Lines and Plans of Closest Fit to System of Points in Space Philos[J]. Magnetism,1902,52(6):11-15.
[122]FINNEY C E A, GREEN J B J, DAW C S. Symbolic Time-series Analysis of Engine Combustion Measurements[J]. SAE Paper No.980624.
[123]蔡觉平,李赞,宋文涛.一种混沌伪随机序列复杂度分析法[J].物理学报,2003,52(8)：1871-1876.
[124]金宁德,李伟波.非线性时间序列的符号化分析方法研究[J].动力学与控制学报,2004,2(3)：54-59.
[125]Kurths J, Schwarz U, Witt A, et al. Measures of Complexity in Signal Analysis[J]. Proc of the AIP Conference on Chaotic, Fractal, and Nonlinear Signal Processing. Woodbury, USA, 1996:33-54.
[126]Ray A. Symbolic Dynamic Analysis of Complex Systems for Anomaly Detection[J]. Signal Processing,2004,84(7):1115-1130.
[127]孟祥星,王宏.大规模风电并网条件下的电力系统调度[J].东北电力大学学报,2009,29(1)：1-7.
[128]李筠,祝勇.数据处理的Beta分布拟合法[J].仪器仪表学报,2004,25(4)：762-763.
[129]Bludszuweit, H.; Dominguez-Navarro, J.A.; Llombart, A., Statistical Analysis of Wind Power Forecast Error[J] Power Systems, IEEE Transactions on, vol.23, no.3, pp.983,991, Aug.2008.
[130]Bludszuweit, H.; Dominguez-Navarro, J.A., A Probabilistic Method for Energy Storage Sizing Based on Wind Power Forecast Uncertainty[J]Power Systems, IEEE Transactions on, vol.26, no.3, pp.1651,1658, Aug.2011.
[131]Papadopolos M, Malatestas P, Hatziagyriou N. Simulation and Analysis of Small and Medium Size Power Systems Containing Wind Turbines[J]. IEEE Trans on Power systems, 1991,6(4):1453-1458.
[132]Saad S Z, Jenkins N. Models for Predicting Flicker Induced by Large Wind Turbines[J]. IEEE Trans on Power Systems,1999,14(3):743-748.
[133]Andres E Feijoo, JosoeCidras. Modeling of Wind Farms in the Load Flow Analysis[J]. IEEE Transactions on Power System,2000,15(1):110-115.
[134]G. S. Stavrakakis, GN. Kariniotakis. A General Simulation Algorithm for the Accurate Assessment of Island Diesel-Wind Turbines System Interaction[J]. IEEE Transactions on Energy Conversion,1995,10(3):577-553.
[135]李建林,许洪华,高志刚,胡书举,朱颖.风力发电中的电力电子变流技术[M].北京：机械工业出版社,2008.
[136]闫永勤,乔明,林飞等.变速恒频风力发电系统最大风能跟踪控制的研究[J].电气技术,2006,11：14-17.
[137]南晓强,李群湛,刘航.含风电系统线路负载率的概率计算法[J].电力系统及其自动化学报,2012,24(4)：126-129.
[138]申洪,王伟胜,戴慧珠.变速恒频风力发电机组的无功功率极限[J].电网技术,2003,27(11)：60-63.
[139]陈树勇,戴慧珠,白晓民,等.风电场的发电可靠性模型及其应用[J].中国电机工程学报,2000,20(3)：26-29.
[140]Matthias Lange, Detlev Heinemann. Relating the Uncertainty of Short-term Wind Speed Predictions to Meteorological Situations with Methods from Synoptic Climatology[J]. European Wind Energy Conference&Exhibition EWEC 2003, Madrid, Spain.
[141]Matthias Lange. On the Uncertainty of Wind Power Predictions-analysis of the Forecast Accuracy and Statistical Distribution of Errors[J]. Journal of Solar Energy Engineering,2005, 127:177-184.
[142]P. Pinson, G. N. Kariniotakis. Wind Power Forecasting using Fuzzy Neural Networks Enhanced with On-line Prediction Risk Assessment[J].2003 IEEE Bologna PowerTech Conference, June 23-26, Bologna, Italy.
[143]Luig A., Bofinger S., Beyer H.G.. Analysis of Confidence Intervals for the Prediction of Regional Wind Power Output[J]. Proc.of the 2001 European Wind Energy Association Conference, EWEC'01, Copenhagen(Denmark),2001,2-6 July:725-728.
[144]Lange M., Waldl H.-P. Assessing the Uncertainty of the Wind Power Predictions with Regard to Specific Weather Situations[J]. Proc. of the 2001 European Wind Energy Association Conference, EWEC'01, Copenhagen (Denmark),2001,2-6 July:695-698.
[145]Roy S. Market Constrained Optimal Planning for Wind Energy Conversion Systems Over Multiple Installation Sites[J]. IEEE Trans. on Energy Conversion,2002,17(1):124-129.
[146]周玮,孙辉,顾宏,等.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)：148-154.
[147]韩自奋,陈启卷.考虑约束的风电调度模式[J].电力系统自动化,2010,,3(2)：89-92.
[148]王鹏,任冲,彭明侨.西北电网风电调度运行管理研究[J].电网与清洁能源,2009,25(11)：80-84.