风电预测、协同调度及电网电压安全评估研究
|
摘要
常规化石能源的不断消耗以及引起的碳排放、环境污染等问题日趋严重,寻求清洁高效的可再生资源以代替可耗竭的化石能源已受到人们的普遍关注。利用风电、光伏等可再生资源代替化石能源,对于降低碳排放,改善能源结构将起到重要的作用,但由于可再生资源具有明显的间歇性以及波动性,使可再生能源发电及其并入电网调控的难度不断增加,而且近几年,以降低碳排放、节约常规能源和减少环境污染为目的的电动汽车,随机的不加引导的接入电网进行自由充电,都会使电源侧和负荷侧呈现出一定的非可控性,由此给电网的调控带来了新的挑战。在建设坚强智能电网的背景下,为了应对特高压交直流以及可再生能源和电动汽车的不断广泛接入带来的影响,研究风电并网条件下的电网调控和安全评估,对于降低电网安全稳定运行的风险,提高电网运行的经济性都具有重要的经济和现实意义。
在以上背景下,本文在对风电波动规律分析的基础上,以实现坚强的智能电网为核心,开展了风电功率预测、风电等可再生资源与电动汽车的协同调度以及风电并入电网背景下的电网安全评估的研究,主要工作和创新成果如下：
(1)利用混沌理论揭示了风电功率序列内在的动态特性。在风电功率时间序列相空间重构的基础上计算了风电序列的最大Lyapunov指数,验证了风电时间序列的混沌特性：由于直接采用Volterra滤波器多步预测法对风电序列进行超短期预测误差较大,利用局域多步预测法以及最大Lyapunov指数法的预测结果并结合有序算子和加权马尔科夫链对Volterra滤波器的预测结果进行了校正。仿真结果表明,在实现风电功率的超短期预测过程中,校正预测模型有效的提高了Volterra滤波器的预测精度,其为利用Volterra滤波器多步法进行风电功率超短期预测提供了有益的参考。
(2)针对风电功率的短期预测问题,分别提出了熵和极端学习机以及储备池的风电组合预测模型。首先利用经验模态分解(empirical mode decomposition,EMD)将风电功率分解为一系列具有不同特征尺度的子序列;在此基础上,利用熵对不同尺度的子序列进行复杂度分析,根据子序列的不同熵值进行归类叠加产生新的子序列。最后利用交叉验证法和重构相空间法确定了学习机的各种参数和输入维数,再利用极端学习机、储备池和最小二乘支持向量机分别对各子序列进行建模预测分析,仿真结果表明基于储备池和极端学习机组合预测模型无论在预测精度和训练速度上都明显优于最小二乘支持向量机的组合预测模型,而且相对于混沌理论,其既适用于超短期预测又适用于短期预测,同时又具有较高预测精度,为实现风电在线的较高精度预测提供了可能。
(3)针对风电以及电动汽车的广泛发展,考虑风光预测结果的基础上,提出了上种计及风电出力不确定性的地区电网的电动汽车充电调度方法。首先为了减小地区电网等效负荷峰谷差和购电成本,建立了电动汽车充电的多目标非线性混合整数优化调度模型。其次利用模糊集理论在风电出力模糊化的基础上将多目标模糊优化模型转化为单目标的非线性优化模型。以某地区电网的数据为算例,用改进的粒子群算法对提出的多目标模糊优化模型进行求解,验证了模型的有效性和求解方法的可行性,为风电和电动汽车的协同优化调度提供了一条有效途径。
(4)在风电并入电网会对电网的安全运行产生影响的背景下,在信号能量法的基础上,考虑稳定性理论的超调量和综合考虑超调量和调节时间的思想,提出了充分计及模型动态特性的电网电压稳定性能工程评估的方法,即综合指数和累积指数两种指标。在利用PSS/E的仿真功能得到电网节点电幅出值的信息基础上,考虑分时段的信号能量谱,提出了信号能量综合指数和累积指数指标,以确定电网中电压稳定的薄弱节点。最后利用传统电压稳定分析理论中的方法,对山东电网2010年冬的系统数据进行对比分析,在计及电源和风机模型的前提下,本文方法在实际工程应用中与传统分析方法具有较好的一致性性,可为电网的电压安全的工程应厂用评估提供一种有益的参考。
The problems caused by conventional fossil energy consumption, carbon emissions and environmental pollution are becoming increasingly gravely, thereby, it has been widely attached importance to seek the clean and efficient renewable resources to replace the exhausting fossil energy. It plays an important role that using wind power, photovoltaic and the other renewable resources instead of fossil energy to reduce carbon emission and improve energy structure, however, the difficulty of the renewable resources connected to power grids is increasing for the renewable resources posses obvious intermittency and volatility. In recent years, the plug-in electric vehicles that are considered as decrease carbon emissions, economize conventional energy and reduce environmental pollution on the purpose, are randomly connected into power grids without guidance to charge for free, thereby, all of the above make both the power supply side and the load one emerge as the non-controllability, which bring a new challenge to the regulation of the power grids. So under the context of constructing a strong smart grids, in order to cope with the impact of Ultra High Voltage AC and DC, as well as the renewable resources and plug-in electric vehicles widely connecting to the power grids, carrying out the study on regulation and controlling of the power grids and safety assessment, which is regarded to reduce security and stability operation risk and improve the operation economics of power grids, has important economic and practical significance.
Consequently, on the basis of analyzing wind power fluctuations law, regarding constructing a strong smart power grids as the core, this thesis carries out the following researches, such as wind power prediction, the collaborative dispatching between wind power and plug-in electric vehicles and the security analysis of power grids with wind power integration. The main works and innovative achievements of the thesis are as follows:
(1) Chaotic theory is used to reveal the internal dynamic property of wind power time series. The largest Lyapunov exponent of wind power time series is calculated on the basis of phase space construction to verify the chaotic characteristics of wind power sets. The wind power forecasting would produce larger errors by using the Volterra filter multi-step prediction, thereby, the prediction results of Volterra filter are corrected by combining the ones predicted by Local-region Multi-steps Method and the Largest Lyapunov exponent method with weighted Markov chain and ordered operator, The simulation results illustrate that the correction forecasting model improves high predictive accuracy effectively under carrying out the ultra-short-term wind power prediction, which provides a useful reference for wind power forecasting by using the Volterra filter multi-steps method.
(2) According to the short-term wind power prediction, a combined model of wind power prediction based on entropy, extreme learning machine(ELM) and reservoir(ESN), respectively. The wind power time series is decomposed into a series of sub-sequences with different characteristic scales, which is given a complexity analysis by using entropy to generate a new sub-sequence according to the classifying and superimposing with different entropies sub-sequence. Finally, the parameters and input vector dimensions of each learning machines are determined by cross validation and chaotic phase space theory. Then, the forecasting model of each subsequence is created with least squares support vector machine(LSSVM), ELM and ESN, respectively. The simulation results illustrate that the combining prediction model based on ELM and ESN is better in the training speed and forecasting accuracy than the one based on LSSVM method. In contrast to the chaotic theory, the proposed combining prediction models based on ELM and ESN that not only suit for the ultra-short-term forecasting but also can be used to short-term forecasting, have a higher prediction accuracy and provide a new useful reference for wind power forecasting in online engineering application.
(3) Considering the extensive development of plug-in vehicles(PEVs) and wind power, an approach for PEVs charging dispatching in regional power grids with the uncertain outputs of wind power is proposed based on the wind power forecasting results. Firstly, in order to reduce the difference between the peak and the valley for equivalent load and purchasing power cost, a multi-objective non-linear mixed integer optimization model for PEVs charging dispatching is established. Secondly, the fuzzy theory is introduced to this paper to fuzzy the output of wind power and photovoltaic power. Therefore, the multi-objective fuzzy optimization model is reformulated as a single objective non-linear optimization problem. Thus, the data of example on regional power grid is analyzed to prove to the validity of model and the feasibility of solving for problems with improved particle swarm algorithm. An effective way is provided for the collaborative optimal dispatch of PEVs.
(4) In the context of the influence on the safety and stability operation of power grids produced by wind power connected into power grids, considering the idea of overshoot, both overshoot and adjustment time on stability theory, assessments for voltage stability performance of power grid that contains fully the dynamic model characteristics, such as:signal energy aggregate index(SEAI) and cumulating index(CI), are proposed based on the signal energy method. On the basis of the dynamic simulation capabilities of PSS/E software to obtain the information of voltage amplitude of power grid, the two new criterions named as SEAI and CI are constructed considering different period signal energy spectrums to confirm the weak bus of voltage stability of power grids. Finally, the operation mode data about the winter of2010in Shandong power grid is used to compare to the conventional methods of voltage stability analysis to verify that the proposed methods have better credibility under the premise of full account of the power supply and fan models.
在以上背景下,本文在对风电波动规律分析的基础上,以实现坚强的智能电网为核心,开展了风电功率预测、风电等可再生资源与电动汽车的协同调度以及风电并入电网背景下的电网安全评估的研究,主要工作和创新成果如下：
(1)利用混沌理论揭示了风电功率序列内在的动态特性。在风电功率时间序列相空间重构的基础上计算了风电序列的最大Lyapunov指数,验证了风电时间序列的混沌特性：由于直接采用Volterra滤波器多步预测法对风电序列进行超短期预测误差较大,利用局域多步预测法以及最大Lyapunov指数法的预测结果并结合有序算子和加权马尔科夫链对Volterra滤波器的预测结果进行了校正。仿真结果表明,在实现风电功率的超短期预测过程中,校正预测模型有效的提高了Volterra滤波器的预测精度,其为利用Volterra滤波器多步法进行风电功率超短期预测提供了有益的参考。
(2)针对风电功率的短期预测问题,分别提出了熵和极端学习机以及储备池的风电组合预测模型。首先利用经验模态分解(empirical mode decomposition,EMD)将风电功率分解为一系列具有不同特征尺度的子序列;在此基础上,利用熵对不同尺度的子序列进行复杂度分析,根据子序列的不同熵值进行归类叠加产生新的子序列。最后利用交叉验证法和重构相空间法确定了学习机的各种参数和输入维数,再利用极端学习机、储备池和最小二乘支持向量机分别对各子序列进行建模预测分析,仿真结果表明基于储备池和极端学习机组合预测模型无论在预测精度和训练速度上都明显优于最小二乘支持向量机的组合预测模型,而且相对于混沌理论,其既适用于超短期预测又适用于短期预测,同时又具有较高预测精度,为实现风电在线的较高精度预测提供了可能。
(3)针对风电以及电动汽车的广泛发展,考虑风光预测结果的基础上,提出了上种计及风电出力不确定性的地区电网的电动汽车充电调度方法。首先为了减小地区电网等效负荷峰谷差和购电成本,建立了电动汽车充电的多目标非线性混合整数优化调度模型。其次利用模糊集理论在风电出力模糊化的基础上将多目标模糊优化模型转化为单目标的非线性优化模型。以某地区电网的数据为算例,用改进的粒子群算法对提出的多目标模糊优化模型进行求解,验证了模型的有效性和求解方法的可行性,为风电和电动汽车的协同优化调度提供了一条有效途径。
(4)在风电并入电网会对电网的安全运行产生影响的背景下,在信号能量法的基础上,考虑稳定性理论的超调量和综合考虑超调量和调节时间的思想,提出了充分计及模型动态特性的电网电压稳定性能工程评估的方法,即综合指数和累积指数两种指标。在利用PSS/E的仿真功能得到电网节点电幅出值的信息基础上,考虑分时段的信号能量谱,提出了信号能量综合指数和累积指数指标,以确定电网中电压稳定的薄弱节点。最后利用传统电压稳定分析理论中的方法,对山东电网2010年冬的系统数据进行对比分析,在计及电源和风机模型的前提下,本文方法在实际工程应用中与传统分析方法具有较好的一致性性,可为电网的电压安全的工程应厂用评估提供一种有益的参考。
The problems caused by conventional fossil energy consumption, carbon emissions and environmental pollution are becoming increasingly gravely, thereby, it has been widely attached importance to seek the clean and efficient renewable resources to replace the exhausting fossil energy. It plays an important role that using wind power, photovoltaic and the other renewable resources instead of fossil energy to reduce carbon emission and improve energy structure, however, the difficulty of the renewable resources connected to power grids is increasing for the renewable resources posses obvious intermittency and volatility. In recent years, the plug-in electric vehicles that are considered as decrease carbon emissions, economize conventional energy and reduce environmental pollution on the purpose, are randomly connected into power grids without guidance to charge for free, thereby, all of the above make both the power supply side and the load one emerge as the non-controllability, which bring a new challenge to the regulation of the power grids. So under the context of constructing a strong smart grids, in order to cope with the impact of Ultra High Voltage AC and DC, as well as the renewable resources and plug-in electric vehicles widely connecting to the power grids, carrying out the study on regulation and controlling of the power grids and safety assessment, which is regarded to reduce security and stability operation risk and improve the operation economics of power grids, has important economic and practical significance.
Consequently, on the basis of analyzing wind power fluctuations law, regarding constructing a strong smart power grids as the core, this thesis carries out the following researches, such as wind power prediction, the collaborative dispatching between wind power and plug-in electric vehicles and the security analysis of power grids with wind power integration. The main works and innovative achievements of the thesis are as follows:
(1) Chaotic theory is used to reveal the internal dynamic property of wind power time series. The largest Lyapunov exponent of wind power time series is calculated on the basis of phase space construction to verify the chaotic characteristics of wind power sets. The wind power forecasting would produce larger errors by using the Volterra filter multi-step prediction, thereby, the prediction results of Volterra filter are corrected by combining the ones predicted by Local-region Multi-steps Method and the Largest Lyapunov exponent method with weighted Markov chain and ordered operator, The simulation results illustrate that the correction forecasting model improves high predictive accuracy effectively under carrying out the ultra-short-term wind power prediction, which provides a useful reference for wind power forecasting by using the Volterra filter multi-steps method.
(2) According to the short-term wind power prediction, a combined model of wind power prediction based on entropy, extreme learning machine(ELM) and reservoir(ESN), respectively. The wind power time series is decomposed into a series of sub-sequences with different characteristic scales, which is given a complexity analysis by using entropy to generate a new sub-sequence according to the classifying and superimposing with different entropies sub-sequence. Finally, the parameters and input vector dimensions of each learning machines are determined by cross validation and chaotic phase space theory. Then, the forecasting model of each subsequence is created with least squares support vector machine(LSSVM), ELM and ESN, respectively. The simulation results illustrate that the combining prediction model based on ELM and ESN is better in the training speed and forecasting accuracy than the one based on LSSVM method. In contrast to the chaotic theory, the proposed combining prediction models based on ELM and ESN that not only suit for the ultra-short-term forecasting but also can be used to short-term forecasting, have a higher prediction accuracy and provide a new useful reference for wind power forecasting in online engineering application.
(3) Considering the extensive development of plug-in vehicles(PEVs) and wind power, an approach for PEVs charging dispatching in regional power grids with the uncertain outputs of wind power is proposed based on the wind power forecasting results. Firstly, in order to reduce the difference between the peak and the valley for equivalent load and purchasing power cost, a multi-objective non-linear mixed integer optimization model for PEVs charging dispatching is established. Secondly, the fuzzy theory is introduced to this paper to fuzzy the output of wind power and photovoltaic power. Therefore, the multi-objective fuzzy optimization model is reformulated as a single objective non-linear optimization problem. Thus, the data of example on regional power grid is analyzed to prove to the validity of model and the feasibility of solving for problems with improved particle swarm algorithm. An effective way is provided for the collaborative optimal dispatch of PEVs.
(4) In the context of the influence on the safety and stability operation of power grids produced by wind power connected into power grids, considering the idea of overshoot, both overshoot and adjustment time on stability theory, assessments for voltage stability performance of power grid that contains fully the dynamic model characteristics, such as:signal energy aggregate index(SEAI) and cumulating index(CI), are proposed based on the signal energy method. On the basis of the dynamic simulation capabilities of PSS/E software to obtain the information of voltage amplitude of power grid, the two new criterions named as SEAI and CI are constructed considering different period signal energy spectrums to confirm the weak bus of voltage stability of power grids. Finally, the operation mode data about the winter of2010in Shandong power grid is used to compare to the conventional methods of voltage stability analysis to verify that the proposed methods have better credibility under the premise of full account of the power supply and fan models.
引文
[1]Mcelroy M B, Lu X. Potential for wind-generated electricity in China[J]. Science,2009,325:1378-1380.
[2]刘振亚.大会主旨报告：大力发展特高压技术推动能源利用方式创新与变革//2009特高压输电技术国际会议.2009年5月20日-22日,北京.
[3]王锡凡,方万良,杜正春.现代电力系统[M].北京：科学出版社,2003.
[4]王承煦,张源.风力发电[M].中国电力出版社,2003.
[5]Wegley H, Formica W. Test applications of a semi-objective approach to wind forecasting for wind energy applications[J]. PNL-4403, Pacific Northwest laboratory,1983.
[6]Kaminsky F, et al. Time series models of average wind speed within synoptic weather categories[C]. In:Proceedings of the fourth ASME wind energy symposium; 1985, pp.215-9
[7]Wegley H, Kosorok M, Formica W. Sub-hourly wind forecasting techniques for wind turbine oprations[J]. PNL-4894. Pacific Northwest Laboratory,1984.
[8]Stratton, D. A., Stengel, R F. Robust Kalman filter design for predictive wind shear detection[J], IEEE Transactions on Aerospace and Electronic Systems, 1993,29(4):1185-1194.
[9]Troen I, Petersen E L. European wind atlas[M]. Roskilde:Risoe National Laboratory,1989.
[10]Hipper H S, Pedreira C E, Souza R C. Neural networks for short-term load forecasting a review and evaluation[J]. IEEE Transactions on Power System, 2001, Vol.16(1):44-55.
[11]Damousis I G, Alexiadis M C, John B. A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation[J].IEEE Transactions on Energy Conversion,2004,19(2): 352-361.
[12]EI-FT H M, EI-Saadany E F, Salama M M A. Improved grey predictor rolling models for wind power prediction[J]. IET Generation Transmission Distribution,2007,6(1),928-937.
[13]Cherkassky V. Mulier F. Vapnik-Cherconernkis (VC) learning theory and its application[J]. IEEE Transaction on Neural Networks,1999, Vol.10(5): 985-987.
[14]Mohandes M A, Halawani T O, Rehman S, Hussain Ahmed A. Support Vector Machines for wind speed prediction[J]. Renewable Energy,2004, 29(6):939-947.
[15]冯双磊,王伟胜,刘纯等.风电场功率预测物理方法研究[J].中国电机工程学报,2010,30(2)：1-6.
[16]Ernst B, Reyer F, Vanzetta, J. Wind power and photovoltaic prediction tools for balancing and grid operation[C]. Integration of Wide-Scale Renewable Resources Into the Power Delivery System,2009 CIGRE/IEEE Joint Symposium, Germany 2009:1-9.
[17]杨秀媛,肖洋.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.
[18]范高峰,王伟胜，刘纯,等.基于人工神经网络的风电功率预测[J].中国电机工程学报,2008,28(34)：118-123.
[19]王彩霞,鲁宗相,乔颖,等.基于非参数回归模型的短期风电功率预测[J].电力系统自动化,2010,34(16)：78-82.
[20]李智,韩学山,杨明,等.基于分位点回归的风电功率波动区间分析[J].电力系统自动化,2011,35(3)：83-87.
[21]周松林, 峁美琴,苏建辉.风电功率短期预测及非参数区间估计[J].中国电机工程学报,2011,31(25)：10-16.
[22]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.
[23]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].中国电机工程党报,2011,31(28)：9-13.
[24]王丽婕,冬雷,廖晓钟,等.基于小波分析的风电场短期发电功率预测[J].中国电机工程学报,2009,29(28)：30-33.
[25]王丽婕,冬雷,胡国飞,等.基于多嵌入维数的风力发电功率组合预测 模型[J].控制与决策,2010,25(4)：576-580.
[26]陈宁,沙倩,汤奕,等.基于交叉熵的风电功率组合预测方法[J].中国电机工程学报,2012,32(4)：29-34.
[27]蒋小亮,蒋传文,彭明鸿,等.基于时间连续性及季节周期性的风速短期组合预测方法[J].电力系统自动化,2010，34(15)：75-79.
[28]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.
[29]郭创新,王扬,沈勇,等.风电场短期风速的多变量局域预测法[J].中国电机工程学报,2012,32(1)：24-31.
[30]Alexiadis M C, Dokopoulos P S, Sahsamanoglou H S, et al. Short-term forecasting of wind spees and related electrical power[J]. Solar Energy,1998, Vol.63(1):61-68.
[31]Alexiadis M C, Dokopoulos P S, Sahsamanoglou H S. Wind speed and power forecasting based on spatial correlation models[J]. IEEE Transactions on Energy Conversion,1999, Vol.14(3):836-842.
[32]崔杨,穆刚,刘玉,严干贵.风电功率波动的时空分布特性[J].电网技术,2011,35(2)：110-114.
[33]李智,韩学山,韩力,康凯.地区电网风电场功率超短期预测方法[J].电力系统自动化,2010,34(7)：90-94.
[34]Tastu J, Pinson P, Kotwa E, et al. Spatio-temporal analysis and modeling of short-term wind power forecast errors [J]. Wind Energy,2011,14(1):43-60.
[35]卿湘运,杨富文,王行愚.采用贝叶斯-克里金-卡尔曼模型的多风电场风速短期预测[J].中国电机工程学报,2012,32(35)：107-114.
[36]陈颖,孙荣富,吴志坚,等.基于统计升尺度方法的区域风电场群功率预测[J].电力系统自动化,2010,34(15)：75-79.
[37]Liang R H, Liao J H. A fuzzy-optimization approach for generation scheduling with wind and solar energy systems[J]. IEEE Transactions on Power Systems,2007,22(4):1665-1674.
[38]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.
[39]Hetzer J, Yu D C. An economic dispatch model incorporating wind power[J]. IEEE Transactions on Power Energy Conversion,2008,23(2): 603-611.
[40]陈海炎,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.
[41]陈宁,于继来.基于电气剖分信息的风电系统有功调度与控制[J].中国电机工程学报,2008,28(16)：51-58.
[42]周玮,彭昱,孙辉,魏庆海.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.
[43]孙元章,吴俊,李国杰,何剑.基于风速预测和随即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)：41-47.
[44]袁铁江,晁勤,叶尔逊 伊不拉音,李义岩.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.
[45]王卿然,谢国辉,张粒r.含风电系统的发用电一体化调度模型.电力系统自动化[J],2011,35(5)：15-18.
[46]张伯明,吴文传,郑太,孙宏斌.消纳大规模风电的多时间尺度协调的有功调度系统设计[J]. 电力系统自动化,2011,35(1)：1-6.
[47]张国强,张伯明,吴文传.考虑风电接入的协调滚动发电计划[J].电力系统自动化,2011,35(19)：18-22.
[48]沈伟,吴文传,张伯明,郑太一,孙宏斌.消纳大规模风电的在线滚动调度策略与模型[J].电力系统自动化,2011,35(22)：136-140.
[49]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.
[50]陈道君,龚庆武,乔卉,赵简.采用改进生物地理学算法的风电并网电力系统多目标发电调度[J].中国电机工程学报,2012.32(31)：150-158.
[51]邱威,张建华,刘念.含大型风电场的环境经济调度模型和算法[J].中国电机工程学报,2011,31(19)：8-16.
[52]王锡凡,邵成成,王秀丽,杜超.电动汽车允电负荷与调度控制策略综述[J].中国电机工程学报,2011.33(1)：1-10.
[53]于大洋,宋曙光,张波,韩学山.区域电网电动汽车充电与风电协同调 度的分析[J].电力系统自动化,2011,35(14)：24-29.
[54]赵俊华,文福拴,薛禹胜,等.计及电动汽车和风电出力不确定性的随机经济调度[J].电力系统自动化,2010,34(20)：22-29.
[55]Qian Kejun, Zhou Chengke, Allan M, et al. Modeling of load demand due to EV battery charging in distribution systems[J]. IEEE Transactions on Power Systems,2011,26(2):802-810.
[56]李正烁,孙宏斌,郭庆来,王尧.计及碳排放的输电网侧“风-车协调’研究[J].中国电机工程学报,2012,32(1)：1-10.
[57]王贵斌,赵俊华,文福拴,薛禹胜,等.配电系统中电动汽车与可再生能源的随机协同调度[J].电力系统自动化,2012,36(19)：22-29.
[58]刘志鹏,文福拴,薛禹胜,辛建波.计及可入网电动汽车的分布式电源最优选址和定容[J].电力系统自动化,2011,35(18)：11-16.
[59]苗轶群,江全元,曹一家.基于微电网的电动汽车换电站运营策略[J].电力系统自动化,2012,36(15)：33-38.100.
[60]于大洋,黄海丽,雷鸣,等.电动汽车充电与风电协同调度的碳减排效益分析[J].电力系统自动化,2012,36(10)：14-18.
[61]胡泽春,宋永华,徐智威,等.电动汽车接入电网的影响与利用[J].中国电机工程学报,2012,32(4)：1-10.
[62]刘道伟,广域环境下的电网静态稳定性能态势评估研究[D].济南：山东大学博士学位论文,2011.
[63]IEEE/CIGRE joint task force on stability terms and definitions. Definition and classification of power system stability[J]. IEEE Trans on Power Systems,2004,19(2):1387-1401.
[64]周双喜,朱凌志,郭锡玖,等.电力系统电压稳定性及其控制[M].北京：中国电力出版社,2004.
[65]赵晋泉,王毅,李可文,等.一种基于连续潮流的在线静态稳定综合评估方法[J].电力系统自动化,2010,34(4)：18-22.
[66]Chiang Hsiao-Dong, Rene Jean-Jumeau. A more efficient formulation for computation of the maximum loading points in electric power system[J]. IEEE Transactions on Power Systems,1995,10(2):633-645.
[67]段献忠,袁骏,何仰赞,张德泉.电力系统电压稳定灵敏度分析方法[J].电力系统自动化,1997,21(4)：9-12.
[68]杨小煜,周孝信,李立新.利用混合块消去算法的电压稳定负荷裕度灵敏度计算[J].中国电机工程学报,2012,32(31)：134-141.
[69]Flueck AJ, Gonella R, Dondeti J R. A newpower sensitivity method of ranking branch outage contingencies for voltage collapse[J]. IEEE Transactions on Power Systems,2002,17(2):263-270.
[70]黄义隆,谢敏,刘明波.基于修正轨迹灵敏度的模型预测长期电压稳定控制[J].电力系统自动化,2012,36(3)：28-33.
[71]冯治鸿,刘取,倪以信,黄眉.多机电力系统电压静态稳定分析-奇异值分解法[J].中国电机工程学报,1992,12(3)：10-19.
[72]B. Gao, G. K. Morison, and P. Ktmdar. Voltage Stability Evaluation Using Modal Analysis[J]. IEEE Transactions on Power Systems, Vol.7, No.4, Nov.1992,1529-1543.
[73]Marceau R J, Soumare S. A unified approach for estimating transient and long-term stability transfer limits[J]. IEEE Transaction on Power Systems, 1999,14(2):693-701.
[74]师洪涛,杨静玲,丁茂生,等.基于小波-BP神经网络的短期风电功率预测方法[J].电力系统自动化,2011,35(16)：44-48.
[75]张勇,关伟.基于最大Lyapunov指数的多变量混沌时间序列预测[J].物理学报,2009,58(2)：756-762.
[76]孟庆芳,张强,穆文英.混沌时间序列多步自适应预测方法[J].物理学报,2006,55(4)：1666·1671.
[77]张家树,肖先赐.混沌时间序列的、'olterra自适应预测[J].物理学报,2000,49(3)：403-408.
[78]张家树,肖先赐.用一种少参数非线性自适应滤波器自适应预测低维混沌时间序列[J].物理学报,2000,49(12)：2332-2339.
[79]杜杰,曹一家,刘志坚,等.混沌时间序列的局域高阶Volterra滤波器多步预测模型[J].物理学报,2009,58(9)：5997-6005.
[80]韩敏.混沌时间序列预测理论与方法[M].中国水利水电出版社,2007.
[81]张永明,齐维贵.供热负荷时间序列混沌特性分析及预报模型研究[J].物理学报,2011,60(10)：100508.
[82]Farmer J D, Sidorowich J J. Predicting chaotic time series[J]. Physical Review Letters,1987,59(8):845.
[83]郑永康.相空间重构与支持向量机结合的短期负荷预测研究[D].成都：西南交通大学博士研究生学位论文,2008.
[84]毛李帆,姚建刚,金永顺,等.中长期电力组合预测模型的理论研究[J].中国电机工程学报,2010,30(16)：53-59.
[85]叶林,刘鹏.基于经验模态分解和支持向量机的短期风电功率组合预测模型[J].中国电机工程学报,2011,31(31)：102-108.
[86]王新迎,韩敏.基于极端学习机的多变量混沌时间序列预测[J].物理学报,2012,6(8)：080507.
[87]Huang G B, Ding X J, Zhou H M. Optimization method based extreme learning machine for classification[J]. Neurocomputing,2010,74:155-163.
[88]Huang G B, Zhou H M, Ding X J, et al. Extreme learning machine for regression and multiclass classification[J]. IEEE Trans. on System, Man and Cybernetics-Part B:Cybernetics,2012,42(2):513-529.
[89]JAEGER H, HAAS H. Harnessing nonlinearity:Predicting chaotic systems and saving energy in wireless communication[J]. Science,2004,304(5667): 78-80.
[90]宋彤,李菡.基于小波回声状态网络的混沌时间序列预测[J].物理学报,2012,61(8)：080506(1-7).
[91]Huang N E, Shen Z, Long S R. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of The Royal Society Soc Lond.,1998,454(1971): 903-995.
[92]彭涛,陈晓宏,庄承彬.基于样本熵的东江月径流序列复杂性分析[J].生态环境学报,2009,18(4)：1379-1382.
[93]Richman J S, Moorman J R. Physiological time-series analysis using approximate entropy and sample entropy[J]. American Journal of Physiological Heat and Circulatory Physiology,2000,278(6):2039-2049.
[94]袁琦,周卫东,李淑芳,等.基于ELM和近似熵的脑电信号检测方法[J].仪器仪表学报,2012,33(3)：514-519.
[95]Huang G B, Zhu Q Y, Siew C K. Extreme learning machine:Theory and application[J]. Neurocomputing,2006,70(1-3):489-501.
[96]Suykens J A K, Vandewalle J. Least squares support vector machine classifiers[J]. Neural Processing Letters,1999,9(3):293-300.
[97]Hoerl A E, Kennard R W. Ridge regression:Biased estimation for nonorthogonal problems[J]. Technometrics,1970,12(1),55-67.
[98]胡爱军,马万里,唐贵基.基于集成经验模态分解和峭度准则的滚动轴承故障特征提取方法[J].中国电机工程学报,2012,32(11)：106-111.
[99]蔡艳平,李艾华,石林锁,等.EMD端点效应的改进型混沌延拓方法及其在机械故障诊断中应用[J].振动与冲击,2()11,30(11)：46-52.
[100]Wu Z H, HUANG N E. Ensemble empirical mode decomposition:a noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis,2009, 1(1):1-41.
[101]韩敏,穆大芸.回声状态网络LM算法及混沌时间序列预测[J].控制与决策,2011,26(10)：1469-1472.1478.
[102]史志伟.混沌时间序列预测与储备池机器学习方法研究[D].大连.大连理工大学,2008.
[103]JAEGER H. Echo state network [J]. Scholarpedia,2007,2(9):2330-2339..
[104]史志伟；韩敏.ESN岭回归学习算法及混沌时间序列预测[J].控制与决策,2007,22(3)：258-261.267.
[105]Yang H, Lu L, Zhou W. A novel optimization sizing model for hybrid solar wind power generation system [J]. Solar Energy,2007,81(1):76-84.
[106]马瑞.电力市场中兼顾环境保护和经济效益的双目标模糊优化短期交易计划新模型[J].中国电机工程学报,2002,22(4)：104-106.
[107]Attaviriyanupap P, Kita H, Tanaka E, et al. A fuzzy-optimization approach to dynamic economic dispatch considering uncertainties[J]. IEEE Trans actions on Power Systems,2004,19(3):1299-1307.
[108]来小康.电动汽车对电网的影响的研究报告[D].北京：中国电力科学研究院,2010.
[109]刘明波,段晓军,赵艳.多目标最优潮流问题的模糊建模及内点解法[J].电力系统自动化,1999,23(14)：37-40.
[110]Liang R H, Liao Jianliao. A fuzzy-optimization approach for generation scheduling with wind and solar energy systems[J]. IEEE Trans actions on Power Systems,2007,22(4):1665-1674.
[111]胡国强,贺仁睦.梯级水电站多目标模糊化调度模型及其求解方法[J].电工技术学报,2007,22(1)：154-158.
[112]姜文,严正.基于一种改进粒子群算法的含风电场电力系统动态经济调度[J].电力系统保护与控制,2010,38(21)：173-177,183.
[113]寇晓丽,刘三阳.基于模拟退火的粒子群算法求解约束优化问题[J].吉林大学学报(工学版),2007,37(1)：136-139.
[114]Attaviriyanupap P, Kita H, Tanaka E, et al. A hybrid EP and SQP for dynamic economic dispatch with nonsmooth fuel cost function [J]. IEEE Transactions on Power Systems,2002,17(2):411-416.
[115]周云龙,张学清,孙斌.应用电导探针技术识别气液两相流流行方法及电导波动信号噪声的辨识[J].传感技术学报,2008,21(10)：1708-1712.
[116]李智.风电规律预测及对电网运行影响的研究[D].济南：山东大学博士学位论文.2011.
[117]韩祯祥,曹一家.电力系统的安全性及防治措施[J].电网技术,2004,28(9)：1-6.
[118]薛禹胜.时空协调的大停电防御框架(二)：广域信息、实时量化分析和自适应优化控制[J].电力系统自动化,2006,30(2)：1-10.
[119]Marceau R J, Soumare S. A unified approach for estimating transient and long-term stability transfer limits[J]. IEEE Transaction on Power Systems, 1999,14(2):693-701.
[120]金敏杰,陈家荣,梅生伟,等.基于改进信号能量法估计暂态稳定极限[J].中国电机工程学报,2004,24(19)：1-6.
[121]汤涌,孙华东,易俊,等.基于全微分的戴维南等效参数跟踪算法[J].中 国电机工程学报,2009,29(13)：48-53.
[122]汪洋,卢继平,李文沅,等.基于局部网络电压相量的等效模型及其电压稳定性指标[J].中国电机工程学报,2008,28(34)：52-59.
[123]刘道伟,韩学山,王勇,等.在线电力系统静态稳定域的研究及其应用[J].中国电机工程学报,2009,29(34)：42-49.
[124]熊宁，程浩忠,胡泽春,等.基于PSS/E的最大负荷极限的求解方法[J].电力系统保护与控制,2008,36(20)：1-4，24.
[125]李红智,罗毓珊,王海军,等.圆弧型与X型开缝翅片空气侧流动与传热特可视化试验[J].化工学报,2008,59(8)：1936-1941.
[2]刘振亚.大会主旨报告：大力发展特高压技术推动能源利用方式创新与变革//2009特高压输电技术国际会议.2009年5月20日-22日,北京.
[3]王锡凡,方万良,杜正春.现代电力系统[M].北京：科学出版社,2003.
[4]王承煦,张源.风力发电[M].中国电力出版社,2003.
[5]Wegley H, Formica W. Test applications of a semi-objective approach to wind forecasting for wind energy applications[J]. PNL-4403, Pacific Northwest laboratory,1983.
[6]Kaminsky F, et al. Time series models of average wind speed within synoptic weather categories[C]. In:Proceedings of the fourth ASME wind energy symposium; 1985, pp.215-9
[7]Wegley H, Kosorok M, Formica W. Sub-hourly wind forecasting techniques for wind turbine oprations[J]. PNL-4894. Pacific Northwest Laboratory,1984.
[8]Stratton, D. A., Stengel, R F. Robust Kalman filter design for predictive wind shear detection[J], IEEE Transactions on Aerospace and Electronic Systems, 1993,29(4):1185-1194.
[9]Troen I, Petersen E L. European wind atlas[M]. Roskilde:Risoe National Laboratory,1989.
[10]Hipper H S, Pedreira C E, Souza R C. Neural networks for short-term load forecasting a review and evaluation[J]. IEEE Transactions on Power System, 2001, Vol.16(1):44-55.
[11]Damousis I G, Alexiadis M C, John B. A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation[J].IEEE Transactions on Energy Conversion,2004,19(2): 352-361.
[12]EI-FT H M, EI-Saadany E F, Salama M M A. Improved grey predictor rolling models for wind power prediction[J]. IET Generation Transmission Distribution,2007,6(1),928-937.
[13]Cherkassky V. Mulier F. Vapnik-Cherconernkis (VC) learning theory and its application[J]. IEEE Transaction on Neural Networks,1999, Vol.10(5): 985-987.
[14]Mohandes M A, Halawani T O, Rehman S, Hussain Ahmed A. Support Vector Machines for wind speed prediction[J]. Renewable Energy,2004, 29(6):939-947.
[15]冯双磊,王伟胜,刘纯等.风电场功率预测物理方法研究[J].中国电机工程学报,2010,30(2)：1-6.
[16]Ernst B, Reyer F, Vanzetta, J. Wind power and photovoltaic prediction tools for balancing and grid operation[C]. Integration of Wide-Scale Renewable Resources Into the Power Delivery System,2009 CIGRE/IEEE Joint Symposium, Germany 2009:1-9.
[17]杨秀媛,肖洋.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.
[18]范高峰,王伟胜，刘纯,等.基于人工神经网络的风电功率预测[J].中国电机工程学报,2008,28(34)：118-123.
[19]王彩霞,鲁宗相,乔颖,等.基于非参数回归模型的短期风电功率预测[J].电力系统自动化,2010,34(16)：78-82.
[20]李智,韩学山,杨明,等.基于分位点回归的风电功率波动区间分析[J].电力系统自动化,2011,35(3)：83-87.
[21]周松林, 峁美琴,苏建辉.风电功率短期预测及非参数区间估计[J].中国电机工程学报,2011,31(25)：10-16.
[22]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.
[23]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].中国电机工程党报,2011,31(28)：9-13.
[24]王丽婕,冬雷,廖晓钟,等.基于小波分析的风电场短期发电功率预测[J].中国电机工程学报,2009,29(28)：30-33.
[25]王丽婕,冬雷,胡国飞,等.基于多嵌入维数的风力发电功率组合预测 模型[J].控制与决策,2010,25(4)：576-580.
[26]陈宁,沙倩,汤奕,等.基于交叉熵的风电功率组合预测方法[J].中国电机工程学报,2012,32(4)：29-34.
[27]蒋小亮,蒋传文,彭明鸿,等.基于时间连续性及季节周期性的风速短期组合预测方法[J].电力系统自动化,2010，34(15)：75-79.
[28]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.
[29]郭创新,王扬,沈勇,等.风电场短期风速的多变量局域预测法[J].中国电机工程学报,2012,32(1)：24-31.
[30]Alexiadis M C, Dokopoulos P S, Sahsamanoglou H S, et al. Short-term forecasting of wind spees and related electrical power[J]. Solar Energy,1998, Vol.63(1):61-68.
[31]Alexiadis M C, Dokopoulos P S, Sahsamanoglou H S. Wind speed and power forecasting based on spatial correlation models[J]. IEEE Transactions on Energy Conversion,1999, Vol.14(3):836-842.
[32]崔杨,穆刚,刘玉,严干贵.风电功率波动的时空分布特性[J].电网技术,2011,35(2)：110-114.
[33]李智,韩学山,韩力,康凯.地区电网风电场功率超短期预测方法[J].电力系统自动化,2010,34(7)：90-94.
[34]Tastu J, Pinson P, Kotwa E, et al. Spatio-temporal analysis and modeling of short-term wind power forecast errors [J]. Wind Energy,2011,14(1):43-60.
[35]卿湘运,杨富文,王行愚.采用贝叶斯-克里金-卡尔曼模型的多风电场风速短期预测[J].中国电机工程学报,2012,32(35)：107-114.
[36]陈颖,孙荣富,吴志坚,等.基于统计升尺度方法的区域风电场群功率预测[J].电力系统自动化,2010,34(15)：75-79.
[37]Liang R H, Liao J H. A fuzzy-optimization approach for generation scheduling with wind and solar energy systems[J]. IEEE Transactions on Power Systems,2007,22(4):1665-1674.
[38]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.
[39]Hetzer J, Yu D C. An economic dispatch model incorporating wind power[J]. IEEE Transactions on Power Energy Conversion,2008,23(2): 603-611.
[40]陈海炎,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.
[41]陈宁,于继来.基于电气剖分信息的风电系统有功调度与控制[J].中国电机工程学报,2008,28(16)：51-58.
[42]周玮,彭昱,孙辉,魏庆海.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.
[43]孙元章,吴俊,李国杰,何剑.基于风速预测和随即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)：41-47.
[44]袁铁江,晁勤,叶尔逊 伊不拉音,李义岩.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.
[45]王卿然,谢国辉,张粒r.含风电系统的发用电一体化调度模型.电力系统自动化[J],2011,35(5)：15-18.
[46]张伯明,吴文传,郑太,孙宏斌.消纳大规模风电的多时间尺度协调的有功调度系统设计[J]. 电力系统自动化,2011,35(1)：1-6.
[47]张国强,张伯明,吴文传.考虑风电接入的协调滚动发电计划[J].电力系统自动化,2011,35(19)：18-22.
[48]沈伟,吴文传,张伯明,郑太一,孙宏斌.消纳大规模风电的在线滚动调度策略与模型[J].电力系统自动化,2011,35(22)：136-140.
[49]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.
[50]陈道君,龚庆武,乔卉,赵简.采用改进生物地理学算法的风电并网电力系统多目标发电调度[J].中国电机工程学报,2012.32(31)：150-158.
[51]邱威,张建华,刘念.含大型风电场的环境经济调度模型和算法[J].中国电机工程学报,2011,31(19)：8-16.
[52]王锡凡,邵成成,王秀丽,杜超.电动汽车允电负荷与调度控制策略综述[J].中国电机工程学报,2011.33(1)：1-10.
[53]于大洋,宋曙光,张波,韩学山.区域电网电动汽车充电与风电协同调 度的分析[J].电力系统自动化,2011,35(14)：24-29.
[54]赵俊华,文福拴,薛禹胜,等.计及电动汽车和风电出力不确定性的随机经济调度[J].电力系统自动化,2010,34(20)：22-29.
[55]Qian Kejun, Zhou Chengke, Allan M, et al. Modeling of load demand due to EV battery charging in distribution systems[J]. IEEE Transactions on Power Systems,2011,26(2):802-810.
[56]李正烁,孙宏斌,郭庆来,王尧.计及碳排放的输电网侧“风-车协调’研究[J].中国电机工程学报,2012,32(1)：1-10.
[57]王贵斌,赵俊华,文福拴,薛禹胜,等.配电系统中电动汽车与可再生能源的随机协同调度[J].电力系统自动化,2012,36(19)：22-29.
[58]刘志鹏,文福拴,薛禹胜,辛建波.计及可入网电动汽车的分布式电源最优选址和定容[J].电力系统自动化,2011,35(18)：11-16.
[59]苗轶群,江全元,曹一家.基于微电网的电动汽车换电站运营策略[J].电力系统自动化,2012,36(15)：33-38.100.
[60]于大洋,黄海丽,雷鸣,等.电动汽车充电与风电协同调度的碳减排效益分析[J].电力系统自动化,2012,36(10)：14-18.
[61]胡泽春,宋永华,徐智威,等.电动汽车接入电网的影响与利用[J].中国电机工程学报,2012,32(4)：1-10.
[62]刘道伟,广域环境下的电网静态稳定性能态势评估研究[D].济南：山东大学博士学位论文,2011.
[63]IEEE/CIGRE joint task force on stability terms and definitions. Definition and classification of power system stability[J]. IEEE Trans on Power Systems,2004,19(2):1387-1401.
[64]周双喜,朱凌志,郭锡玖,等.电力系统电压稳定性及其控制[M].北京：中国电力出版社,2004.
[65]赵晋泉,王毅,李可文,等.一种基于连续潮流的在线静态稳定综合评估方法[J].电力系统自动化,2010,34(4)：18-22.
[66]Chiang Hsiao-Dong, Rene Jean-Jumeau. A more efficient formulation for computation of the maximum loading points in electric power system[J]. IEEE Transactions on Power Systems,1995,10(2):633-645.
[67]段献忠,袁骏,何仰赞,张德泉.电力系统电压稳定灵敏度分析方法[J].电力系统自动化,1997,21(4)：9-12.
[68]杨小煜,周孝信,李立新.利用混合块消去算法的电压稳定负荷裕度灵敏度计算[J].中国电机工程学报,2012,32(31)：134-141.
[69]Flueck AJ, Gonella R, Dondeti J R. A newpower sensitivity method of ranking branch outage contingencies for voltage collapse[J]. IEEE Transactions on Power Systems,2002,17(2):263-270.
[70]黄义隆,谢敏,刘明波.基于修正轨迹灵敏度的模型预测长期电压稳定控制[J].电力系统自动化,2012,36(3)：28-33.
[71]冯治鸿,刘取,倪以信,黄眉.多机电力系统电压静态稳定分析-奇异值分解法[J].中国电机工程学报,1992,12(3)：10-19.
[72]B. Gao, G. K. Morison, and P. Ktmdar. Voltage Stability Evaluation Using Modal Analysis[J]. IEEE Transactions on Power Systems, Vol.7, No.4, Nov.1992,1529-1543.
[73]Marceau R J, Soumare S. A unified approach for estimating transient and long-term stability transfer limits[J]. IEEE Transaction on Power Systems, 1999,14(2):693-701.
[74]师洪涛,杨静玲,丁茂生,等.基于小波-BP神经网络的短期风电功率预测方法[J].电力系统自动化,2011,35(16)：44-48.
[75]张勇,关伟.基于最大Lyapunov指数的多变量混沌时间序列预测[J].物理学报,2009,58(2)：756-762.
[76]孟庆芳,张强,穆文英.混沌时间序列多步自适应预测方法[J].物理学报,2006,55(4)：1666·1671.
[77]张家树,肖先赐.混沌时间序列的、'olterra自适应预测[J].物理学报,2000,49(3)：403-408.
[78]张家树,肖先赐.用一种少参数非线性自适应滤波器自适应预测低维混沌时间序列[J].物理学报,2000,49(12)：2332-2339.
[79]杜杰,曹一家,刘志坚,等.混沌时间序列的局域高阶Volterra滤波器多步预测模型[J].物理学报,2009,58(9)：5997-6005.
[80]韩敏.混沌时间序列预测理论与方法[M].中国水利水电出版社,2007.
[81]张永明,齐维贵.供热负荷时间序列混沌特性分析及预报模型研究[J].物理学报,2011,60(10)：100508.
[82]Farmer J D, Sidorowich J J. Predicting chaotic time series[J]. Physical Review Letters,1987,59(8):845.
[83]郑永康.相空间重构与支持向量机结合的短期负荷预测研究[D].成都：西南交通大学博士研究生学位论文,2008.
[84]毛李帆,姚建刚,金永顺,等.中长期电力组合预测模型的理论研究[J].中国电机工程学报,2010,30(16)：53-59.
[85]叶林,刘鹏.基于经验模态分解和支持向量机的短期风电功率组合预测模型[J].中国电机工程学报,2011,31(31)：102-108.
[86]王新迎,韩敏.基于极端学习机的多变量混沌时间序列预测[J].物理学报,2012,6(8)：080507.
[87]Huang G B, Ding X J, Zhou H M. Optimization method based extreme learning machine for classification[J]. Neurocomputing,2010,74:155-163.
[88]Huang G B, Zhou H M, Ding X J, et al. Extreme learning machine for regression and multiclass classification[J]. IEEE Trans. on System, Man and Cybernetics-Part B:Cybernetics,2012,42(2):513-529.
[89]JAEGER H, HAAS H. Harnessing nonlinearity:Predicting chaotic systems and saving energy in wireless communication[J]. Science,2004,304(5667): 78-80.
[90]宋彤,李菡.基于小波回声状态网络的混沌时间序列预测[J].物理学报,2012,61(8)：080506(1-7).
[91]Huang N E, Shen Z, Long S R. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of The Royal Society Soc Lond.,1998,454(1971): 903-995.
[92]彭涛,陈晓宏,庄承彬.基于样本熵的东江月径流序列复杂性分析[J].生态环境学报,2009,18(4)：1379-1382.
[93]Richman J S, Moorman J R. Physiological time-series analysis using approximate entropy and sample entropy[J]. American Journal of Physiological Heat and Circulatory Physiology,2000,278(6):2039-2049.
[94]袁琦,周卫东,李淑芳,等.基于ELM和近似熵的脑电信号检测方法[J].仪器仪表学报,2012,33(3)：514-519.
[95]Huang G B, Zhu Q Y, Siew C K. Extreme learning machine:Theory and application[J]. Neurocomputing,2006,70(1-3):489-501.
[96]Suykens J A K, Vandewalle J. Least squares support vector machine classifiers[J]. Neural Processing Letters,1999,9(3):293-300.
[97]Hoerl A E, Kennard R W. Ridge regression:Biased estimation for nonorthogonal problems[J]. Technometrics,1970,12(1),55-67.
[98]胡爱军,马万里,唐贵基.基于集成经验模态分解和峭度准则的滚动轴承故障特征提取方法[J].中国电机工程学报,2012,32(11)：106-111.
[99]蔡艳平,李艾华,石林锁,等.EMD端点效应的改进型混沌延拓方法及其在机械故障诊断中应用[J].振动与冲击,2()11,30(11)：46-52.
[100]Wu Z H, HUANG N E. Ensemble empirical mode decomposition:a noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis,2009, 1(1):1-41.
[101]韩敏,穆大芸.回声状态网络LM算法及混沌时间序列预测[J].控制与决策,2011,26(10)：1469-1472.1478.
[102]史志伟.混沌时间序列预测与储备池机器学习方法研究[D].大连.大连理工大学,2008.
[103]JAEGER H. Echo state network [J]. Scholarpedia,2007,2(9):2330-2339..
[104]史志伟；韩敏.ESN岭回归学习算法及混沌时间序列预测[J].控制与决策,2007,22(3)：258-261.267.
[105]Yang H, Lu L, Zhou W. A novel optimization sizing model for hybrid solar wind power generation system [J]. Solar Energy,2007,81(1):76-84.
[106]马瑞.电力市场中兼顾环境保护和经济效益的双目标模糊优化短期交易计划新模型[J].中国电机工程学报,2002,22(4)：104-106.
[107]Attaviriyanupap P, Kita H, Tanaka E, et al. A fuzzy-optimization approach to dynamic economic dispatch considering uncertainties[J]. IEEE Trans actions on Power Systems,2004,19(3):1299-1307.
[108]来小康.电动汽车对电网的影响的研究报告[D].北京：中国电力科学研究院,2010.
[109]刘明波,段晓军,赵艳.多目标最优潮流问题的模糊建模及内点解法[J].电力系统自动化,1999,23(14)：37-40.
[110]Liang R H, Liao Jianliao. A fuzzy-optimization approach for generation scheduling with wind and solar energy systems[J]. IEEE Trans actions on Power Systems,2007,22(4):1665-1674.
[111]胡国强,贺仁睦.梯级水电站多目标模糊化调度模型及其求解方法[J].电工技术学报,2007,22(1)：154-158.
[112]姜文,严正.基于一种改进粒子群算法的含风电场电力系统动态经济调度[J].电力系统保护与控制,2010,38(21)：173-177,183.
[113]寇晓丽,刘三阳.基于模拟退火的粒子群算法求解约束优化问题[J].吉林大学学报(工学版),2007,37(1)：136-139.
[114]Attaviriyanupap P, Kita H, Tanaka E, et al. A hybrid EP and SQP for dynamic economic dispatch with nonsmooth fuel cost function [J]. IEEE Transactions on Power Systems,2002,17(2):411-416.
[115]周云龙,张学清,孙斌.应用电导探针技术识别气液两相流流行方法及电导波动信号噪声的辨识[J].传感技术学报,2008,21(10)：1708-1712.
[116]李智.风电规律预测及对电网运行影响的研究[D].济南：山东大学博士学位论文.2011.
[117]韩祯祥,曹一家.电力系统的安全性及防治措施[J].电网技术,2004,28(9)：1-6.
[118]薛禹胜.时空协调的大停电防御框架(二)：广域信息、实时量化分析和自适应优化控制[J].电力系统自动化,2006,30(2)：1-10.
[119]Marceau R J, Soumare S. A unified approach for estimating transient and long-term stability transfer limits[J]. IEEE Transaction on Power Systems, 1999,14(2):693-701.
[120]金敏杰,陈家荣,梅生伟,等.基于改进信号能量法估计暂态稳定极限[J].中国电机工程学报,2004,24(19)：1-6.
[121]汤涌,孙华东,易俊,等.基于全微分的戴维南等效参数跟踪算法[J].中 国电机工程学报,2009,29(13)：48-53.
[122]汪洋,卢继平,李文沅,等.基于局部网络电压相量的等效模型及其电压稳定性指标[J].中国电机工程学报,2008,28(34)：52-59.
[123]刘道伟,韩学山,王勇,等.在线电力系统静态稳定域的研究及其应用[J].中国电机工程学报,2009,29(34)：42-49.
[124]熊宁，程浩忠,胡泽春,等.基于PSS/E的最大负荷极限的求解方法[J].电力系统保护与控制,2008,36(20)：1-4，24.
[125]李红智,罗毓珊,王海军,等.圆弧型与X型开缝翅片空气侧流动与传热特可视化试验[J].化工学报,2008,59(8)：1936-1941.