计及多因素的含风能电力系统可靠性评估及优化研究
|
摘要
随着传统能源资源的日益枯竭及其污染气体的严重排放，人们正在积极寻找能够满足人类能源需求的可替代能源。风能作为一种清洁的可再生能源，是最主要的可替代能源之一。世界上许多国家已出台支持开发风能资源的相应政策，加之风力发电技术的日趋成熟，风能的开发规模也越来越大。风速的随机性和间歇性使得风力发电功率具有波动性和不可控性，大规模风电并网将对电网的安全性和可靠性产生影响。因此，研究风电系统可靠性评估模型、分析风电场对电力系统可靠性的综合影响等，对含风电场的电力系统设计、规划和运行等具有重要的理论指导意义和实际应用价值。
随着风电场规模的扩大、数目的增多、集电系统拓扑的扩展以及风电机组密集度的增加，诸多因素，如：尾流效应、单风电场风的条件相依性、多风电场风的多重相关性、集电系统故障、风电场风机布局等，对风电场年发电量及可靠性的影响也更加凸显。基于此，本文结合国家自然科学基金“风电场可靠性评估的概率模型及算法”（51077135）和重庆市杰出青年基金“大规模风电系统可靠性评估及优化配置研究”（CSTC2010BA3006）项目，对计及前述因素的风速模型、风电场可靠性评估、风电场可靠性优化等问题进行了深入研究。
风的大小和方向是风速尾流模型的关键影响因素，而尾流效应对风电场年发电量、可靠性等有很大影响。风速与风向并非完全独立的随机变量，它们之间蕴含有一定的条件相依性，因此，在建立风速和风向模型时需要计及它们之间的条件相依性，以提高风电场可靠性评估的精确性。基于此，本文基于风向经验概率分布函数，建立风向样本生成模型；基于尾流效应原理和风速Weibull模型以及风速与风向之间的条件相依性，建立计及尾流效应和风向分布的风速条件相依模型，进而生成具有条件相依性的风速与风向。实测风速和风向数据验证了该模型的有效性和实用性。
多个风电场的风速、风向间常存在多重多维相关性关系。当多个风电场接入电网时，模拟具有多重多维相关性的风速和风向样本是进行可靠性评估的先决条件。本文根据Copula理论分别建立风电场风速、风向的正态Copula函数和Archimedean Copula函数，利用经验Copula函数对模型进行选择。采用风向经验分布和风速Weibull分布模型及聚类、变换和插值方法建立风速与风向的条件相依模型，进而建立具有多重多维相关性的风速和风向数据生成模型。通过实际观测风速和风向数据对所提方法和模型进行了验证，结果表明模拟数据能够保持与原始数据相同的相关性、条件相依性、数字特征和分布特性。
风电场中除风电机组的故障影响风电场发电量外，其它因素，如：集电线路故障、开关设备的隔离和切换操作、集电系统拓扑结构等，均对风电场发电量有不同程度的影响，现有研究鲜有计及该类因素的综合影响评估风电系统可靠性。本文结合风电场集电系统的结构特点，提出集电系统分块概念和风电场集电系统可靠性的指标。计及集电系统元件的多重故障以及开关设备的故障隔离、切换及隔离范围等因素影响，利用分块枚举法和矩阵运算等建立风电场集电系统的多状态概率模型，进而得到风电场集电系统可靠性评估模型。分别对四种典型结构的风电场集电系统进行可靠性评估，算例验证了方法的可行性和有效性，结论表明该方法对风电场集电系统拓扑结构规划具有重要的参考价值。
由于尾流效应影响和风电场地形限制等因素，风电场中不同风机布局对应的风电场发电量不尽相同。在风电场规划阶段，为提高风电场年发电量和系统充裕性需要根据区域内风的分布特性和尾流原理优化风电场最优风机布局。本文根据几何学理论确定风电机组尾流区域和计算风电机组之间的水平距离和垂直距离；计及区域风速与风向的联合分布特性，以最大化风电场发电量为目标函数，利用带收缩因子和变异因子的改进粒子群优化算法优化风电场最优风机布局。将该优化模型应用到实际区域的风电场布局规划中，并将优化后的风电场接入到可靠性测试系统分析风电场布局优化的可靠性效益。算例表明优化后的风电场能有效降低尾流效应的影响，增加风电场的发电量，改善系统的可靠性。
风力发电与传统机组发电的主要差别是风电具有波动性和不可调度性。现有含风电场电力系统可靠性的研究能有效计及风电对电网的充裕性贡献，但鲜有能计及风电功率波动对电网可靠性的不利影响。电力系统在稳态运行中，系统既要实时满足负荷功率需求，还要在发电或负荷功率波动时做实时调整，基于电网稳态运行特性，本文计及电网的静态频率特性探讨了能够刻画风电功率波动因素的含风电场发电系统可靠性评估模型。采用负荷等值、机组状态模拟、系统静态频率调节等方法建立评估模型，并模拟系统稳态运行和负荷削减过程。将该模型应用于可靠性测试系统，算例表明该模型不仅能够反映风电功率对电力系统可靠性的功率贡献还能反映风电功率波动对电力系统可靠性的负面影响。
With the increasing exhaustion of traditional energy resources and emission ofnon-environmentally friendly gases, people are actively exploring alternative energyresources which can be used to meet the human energy demand. As a clean andrenewable energy, wind energy is one of the most important alternative energy resources.In the world, many countries have made different policies for encouraging thedevelopment of wind energy resources. With the development of wind power generationtechniques, the scale of wind energy was increasing step by step in the last decade.Generally, wind is considered as a fluctuant and uncontrollable energy resouce due tothe stochastic and intermittent natures of wind speed. Integration of large scale windfarms into a power grid has a significant effect on the security and reliability of powergrid. Therefore, it is necessary for the experts to study the reliabliey assessment modelof wind power systems and to analyze the comprehensive impacts of wind farm on thereliability of power systems. These researches have an important theoretical andpractical significance in the designing, planning and operation of power systemcontaining wind farms.
With the scale expanding of wind farm, increasing the number of wind farm,extending collector system topology, and increasing wind turbine generator (WTG)intensity in a wind farm, mutilple factors, such as wind speed wake, multi-dimensionalcorrelations, collector system failures, and wind farm layout, have significant impactson energy production of wind farms and reliability of power systems. The papertheoretically researches the impact of above factors on wind energy production andwind power system reliability under the support in part by the National Natural ScienceFoundation of China (No.51077135)“Probabilistic model and algorithm of wind farmreliability evaluation” and the Natural Science Foundation Project of CQ CSTC (No.CSTC2010BA3006)“Reliability evaluation and optimization models and algorithmsfor power system containing large scale wind energies”.
Wind speed and wind direction are the critical influence factors of wake effect,which has a very significant effect on wind energy production of wind farm. Wind speedand wind direction are not completely independent variables; on the contrary, they havea certain conditional dependence relationship between them. Therefore, this conditionaldependence should be considered in the proposed wind speed model to improve theaccuracy. According to the above analysis, wind direction sampling model is built using the empirical distribution of wind direction. A conditionally dependent wind speedmodel is established using the wind wake theory, Weibull model of wind speeds, andconditional dependence between wind speeds and wind directions. The proposed modelcan be used to generate wind speed and wind direction data with a given conditionaldependence. The collected wind speed and wind direction data in the practical regionhave been used to verify the validity and applicability of the proposed model.
Correlation and conditional dependence of wind speed and wind direction seriesalways appear in multiple wind farms, which should also be considered in reliabilityevaluation models of power systems containing wind farm. Wind speed correlationmodel and wind direction correlation model are built using normal copula andArchimedean copula in this paper. The empirical copula is used to select a propriatecorrelation model. Clustering method, transformation and interpolation techniques areused to build the conditional dependence model of wind speed and wind direction basedon empirical distribution and Weibull distribution models. Then, wind speed and winddirection generation model considering their correlation and conditional dependence isobtained. The proposed model is verified using the observed wind data, and resultsshow that simulated and observed wind data have an identical features in correlation,conditional dependence, statistics characteristics, and distribution characteristics. Inother words, the results have verified the validity and availability of the proposedmodel.
Besides WTG failures in a wind farm, other factors, such as the failures ofelectrical collector system (ECS), disconnection and switching operation of switchdevices, and different collector system topologies, also have effects on energyproduction of a wind farm. There are relatively little studies on the comprehensiveimpact on the reliability of wind power system. Novel indices for describing thereliability of wind farm ECS are presented based on the topologies of wind farm in thispaper. The concept of section for a partitioning wind farm ECS is defined. A reliabilityevaluation model of wind farm ECS is built using the state enumeration algorithm andmatrix operations. The proposed model can not only considers the multi-failure of ECScomponents, including failures of cable feeder, WTG and wind turbine transformer, butalso consider the operational states of switching devices in failure, disconnection andswitching processes. Four different wind farm ECS topologies are implemented usingproposed model. Case studies on the reliability evaluation of wind farm ECS are used toverify the feasibility and validity of the proposed technique. The proposed technique isvery significant and valuable on wind farm collector system planning.
Energy production of a wind farm is considerably affected by wake effect.Therefore, in wind farm planning, it is essential to optimize the WTGs’ layout fordecreasing the effect of wake effect and increasing the energy production of wind farm.A geometric theory is used to determine the upstream WTGs and calculate thehorizontal and deviation distances between any two WTGs along the wind direction. Aparticle swarm optimization algorithm with the shrinkage factor and mutation factor isused to obtain the optimal solution with maximum energy production of a wind farm.The discrete joint distribution probability of wind direction and wind speed isincorporated into the optimization process. Based on the proposed technique, theoptimal wind farm layout can be obtained using the historical wind speed and winddirection data of the practical region. A modified reliability test system containing awind farm with the optimal layout is used to evaluate the reliability of a generationsystem. The results show that the proposed technique is effective to decrease the wakeeffect, to increase the energy production of wind farm, and to improve the reliability ofpower system.
Compared with conventional generators, wind power is fluctuant andnon-dispatched. The existing researches on reliability of power system with wind farmcan always consider the contribution of wind power on power system adequacy, butthese researches cannot consider the negative impact of wind power fluctuation onpower system reliability. When the power system operates in the steady-state, the taskof power system is not only to meet load demand, but also to implement real timeadjustment when generation side or demand side is suffered from a disturbance.Therefore, according to the steady state operation characteristic of power system, thepaper presents a reliability evaluation model of power system with wind farmconsidering steady-state frequency characteristics. The fluctuation of wind power can bereflected by the proposed model. The model is built by the load power equivalence, thestate simulation of conventional generator, and the steady-state frequency adjustment.The proposed model is applied to reliability test system, and the results show that theproposed method can not only reflect the contribution of wind power to the overallpower system reliability, but also reflect the negative impact of wind power fluctuationon power system reliability.
随着风电场规模的扩大、数目的增多、集电系统拓扑的扩展以及风电机组密集度的增加，诸多因素，如：尾流效应、单风电场风的条件相依性、多风电场风的多重相关性、集电系统故障、风电场风机布局等，对风电场年发电量及可靠性的影响也更加凸显。基于此，本文结合国家自然科学基金“风电场可靠性评估的概率模型及算法”（51077135）和重庆市杰出青年基金“大规模风电系统可靠性评估及优化配置研究”（CSTC2010BA3006）项目，对计及前述因素的风速模型、风电场可靠性评估、风电场可靠性优化等问题进行了深入研究。
风的大小和方向是风速尾流模型的关键影响因素，而尾流效应对风电场年发电量、可靠性等有很大影响。风速与风向并非完全独立的随机变量，它们之间蕴含有一定的条件相依性，因此，在建立风速和风向模型时需要计及它们之间的条件相依性，以提高风电场可靠性评估的精确性。基于此，本文基于风向经验概率分布函数，建立风向样本生成模型；基于尾流效应原理和风速Weibull模型以及风速与风向之间的条件相依性，建立计及尾流效应和风向分布的风速条件相依模型，进而生成具有条件相依性的风速与风向。实测风速和风向数据验证了该模型的有效性和实用性。
多个风电场的风速、风向间常存在多重多维相关性关系。当多个风电场接入电网时，模拟具有多重多维相关性的风速和风向样本是进行可靠性评估的先决条件。本文根据Copula理论分别建立风电场风速、风向的正态Copula函数和Archimedean Copula函数，利用经验Copula函数对模型进行选择。采用风向经验分布和风速Weibull分布模型及聚类、变换和插值方法建立风速与风向的条件相依模型，进而建立具有多重多维相关性的风速和风向数据生成模型。通过实际观测风速和风向数据对所提方法和模型进行了验证，结果表明模拟数据能够保持与原始数据相同的相关性、条件相依性、数字特征和分布特性。
风电场中除风电机组的故障影响风电场发电量外，其它因素，如：集电线路故障、开关设备的隔离和切换操作、集电系统拓扑结构等，均对风电场发电量有不同程度的影响，现有研究鲜有计及该类因素的综合影响评估风电系统可靠性。本文结合风电场集电系统的结构特点，提出集电系统分块概念和风电场集电系统可靠性的指标。计及集电系统元件的多重故障以及开关设备的故障隔离、切换及隔离范围等因素影响，利用分块枚举法和矩阵运算等建立风电场集电系统的多状态概率模型，进而得到风电场集电系统可靠性评估模型。分别对四种典型结构的风电场集电系统进行可靠性评估，算例验证了方法的可行性和有效性，结论表明该方法对风电场集电系统拓扑结构规划具有重要的参考价值。
由于尾流效应影响和风电场地形限制等因素，风电场中不同风机布局对应的风电场发电量不尽相同。在风电场规划阶段，为提高风电场年发电量和系统充裕性需要根据区域内风的分布特性和尾流原理优化风电场最优风机布局。本文根据几何学理论确定风电机组尾流区域和计算风电机组之间的水平距离和垂直距离；计及区域风速与风向的联合分布特性，以最大化风电场发电量为目标函数，利用带收缩因子和变异因子的改进粒子群优化算法优化风电场最优风机布局。将该优化模型应用到实际区域的风电场布局规划中，并将优化后的风电场接入到可靠性测试系统分析风电场布局优化的可靠性效益。算例表明优化后的风电场能有效降低尾流效应的影响，增加风电场的发电量，改善系统的可靠性。
风力发电与传统机组发电的主要差别是风电具有波动性和不可调度性。现有含风电场电力系统可靠性的研究能有效计及风电对电网的充裕性贡献，但鲜有能计及风电功率波动对电网可靠性的不利影响。电力系统在稳态运行中，系统既要实时满足负荷功率需求，还要在发电或负荷功率波动时做实时调整，基于电网稳态运行特性，本文计及电网的静态频率特性探讨了能够刻画风电功率波动因素的含风电场发电系统可靠性评估模型。采用负荷等值、机组状态模拟、系统静态频率调节等方法建立评估模型，并模拟系统稳态运行和负荷削减过程。将该模型应用于可靠性测试系统，算例表明该模型不仅能够反映风电功率对电力系统可靠性的功率贡献还能反映风电功率波动对电力系统可靠性的负面影响。
With the increasing exhaustion of traditional energy resources and emission ofnon-environmentally friendly gases, people are actively exploring alternative energyresources which can be used to meet the human energy demand. As a clean andrenewable energy, wind energy is one of the most important alternative energy resources.In the world, many countries have made different policies for encouraging thedevelopment of wind energy resources. With the development of wind power generationtechniques, the scale of wind energy was increasing step by step in the last decade.Generally, wind is considered as a fluctuant and uncontrollable energy resouce due tothe stochastic and intermittent natures of wind speed. Integration of large scale windfarms into a power grid has a significant effect on the security and reliability of powergrid. Therefore, it is necessary for the experts to study the reliabliey assessment modelof wind power systems and to analyze the comprehensive impacts of wind farm on thereliability of power systems. These researches have an important theoretical andpractical significance in the designing, planning and operation of power systemcontaining wind farms.
With the scale expanding of wind farm, increasing the number of wind farm,extending collector system topology, and increasing wind turbine generator (WTG)intensity in a wind farm, mutilple factors, such as wind speed wake, multi-dimensionalcorrelations, collector system failures, and wind farm layout, have significant impactson energy production of wind farms and reliability of power systems. The papertheoretically researches the impact of above factors on wind energy production andwind power system reliability under the support in part by the National Natural ScienceFoundation of China (No.51077135)“Probabilistic model and algorithm of wind farmreliability evaluation” and the Natural Science Foundation Project of CQ CSTC (No.CSTC2010BA3006)“Reliability evaluation and optimization models and algorithmsfor power system containing large scale wind energies”.
Wind speed and wind direction are the critical influence factors of wake effect,which has a very significant effect on wind energy production of wind farm. Wind speedand wind direction are not completely independent variables; on the contrary, they havea certain conditional dependence relationship between them. Therefore, this conditionaldependence should be considered in the proposed wind speed model to improve theaccuracy. According to the above analysis, wind direction sampling model is built using the empirical distribution of wind direction. A conditionally dependent wind speedmodel is established using the wind wake theory, Weibull model of wind speeds, andconditional dependence between wind speeds and wind directions. The proposed modelcan be used to generate wind speed and wind direction data with a given conditionaldependence. The collected wind speed and wind direction data in the practical regionhave been used to verify the validity and applicability of the proposed model.
Correlation and conditional dependence of wind speed and wind direction seriesalways appear in multiple wind farms, which should also be considered in reliabilityevaluation models of power systems containing wind farm. Wind speed correlationmodel and wind direction correlation model are built using normal copula andArchimedean copula in this paper. The empirical copula is used to select a propriatecorrelation model. Clustering method, transformation and interpolation techniques areused to build the conditional dependence model of wind speed and wind direction basedon empirical distribution and Weibull distribution models. Then, wind speed and winddirection generation model considering their correlation and conditional dependence isobtained. The proposed model is verified using the observed wind data, and resultsshow that simulated and observed wind data have an identical features in correlation,conditional dependence, statistics characteristics, and distribution characteristics. Inother words, the results have verified the validity and availability of the proposedmodel.
Besides WTG failures in a wind farm, other factors, such as the failures ofelectrical collector system (ECS), disconnection and switching operation of switchdevices, and different collector system topologies, also have effects on energyproduction of a wind farm. There are relatively little studies on the comprehensiveimpact on the reliability of wind power system. Novel indices for describing thereliability of wind farm ECS are presented based on the topologies of wind farm in thispaper. The concept of section for a partitioning wind farm ECS is defined. A reliabilityevaluation model of wind farm ECS is built using the state enumeration algorithm andmatrix operations. The proposed model can not only considers the multi-failure of ECScomponents, including failures of cable feeder, WTG and wind turbine transformer, butalso consider the operational states of switching devices in failure, disconnection andswitching processes. Four different wind farm ECS topologies are implemented usingproposed model. Case studies on the reliability evaluation of wind farm ECS are used toverify the feasibility and validity of the proposed technique. The proposed technique isvery significant and valuable on wind farm collector system planning.
Energy production of a wind farm is considerably affected by wake effect.Therefore, in wind farm planning, it is essential to optimize the WTGs’ layout fordecreasing the effect of wake effect and increasing the energy production of wind farm.A geometric theory is used to determine the upstream WTGs and calculate thehorizontal and deviation distances between any two WTGs along the wind direction. Aparticle swarm optimization algorithm with the shrinkage factor and mutation factor isused to obtain the optimal solution with maximum energy production of a wind farm.The discrete joint distribution probability of wind direction and wind speed isincorporated into the optimization process. Based on the proposed technique, theoptimal wind farm layout can be obtained using the historical wind speed and winddirection data of the practical region. A modified reliability test system containing awind farm with the optimal layout is used to evaluate the reliability of a generationsystem. The results show that the proposed technique is effective to decrease the wakeeffect, to increase the energy production of wind farm, and to improve the reliability ofpower system.
Compared with conventional generators, wind power is fluctuant andnon-dispatched. The existing researches on reliability of power system with wind farmcan always consider the contribution of wind power on power system adequacy, butthese researches cannot consider the negative impact of wind power fluctuation onpower system reliability. When the power system operates in the steady-state, the taskof power system is not only to meet load demand, but also to implement real timeadjustment when generation side or demand side is suffered from a disturbance.Therefore, according to the steady state operation characteristic of power system, thepaper presents a reliability evaluation model of power system with wind farmconsidering steady-state frequency characteristics. The fluctuation of wind power can bereflected by the proposed model. The model is built by the load power equivalence, thestate simulation of conventional generator, and the steady-state frequency adjustment.The proposed model is applied to reliability test system, and the results show that theproposed method can not only reflect the contribution of wind power to the overallpower system reliability, but also reflect the negative impact of wind power fluctuationon power system reliability.
引文
[1]国家能源局.可再生能源发展“十二五”规划[R].2012.
[2] Global Wind Energy Council (GWEC). Global wind report annual market update [EB/OL].http://www.gwec.net,2012.
[3]李俊伟,蔡丰波,乔黎明,等.2013中国风电发展报告[R].2013.
[4]周家启、蒋锦峰、孙渝江、谢开贵.电力可靠性基本名词术语[S].北京：中国电力出版社.
[5]朱永强,张旭.风电场电气系统[M].北京:机械工业出版社,2010.
[6]李玉敦.计及相关性的风速模型及其在发电系统可靠性评估中的应用[D].重庆:重庆大学,2012.
[7]姚兴佳,宋俊.机械工业出版社风力发电机组原理与应用[M].北京:机械工业出版社,2009.
[8]国家电力监管委员会电力可靠性管理中心.电力可靠性技术与管理培训教材[M].北京：中国电力出版社,2007.
[9]郭永基.电力系统可靠性分析[M].北京：清华大学出版社,2003.
[10]李文沅.电力系统风险评估：模型,方法和应用[M].北京：科学出版社,2006.
[11] J. A. Carta, P. Ramirez, S. Velazquez. A review of wind speed probability distributions used inwind energy analysis: case studies in the Canary Islands [J]. Renewable and SustainableEnergy Reviews,2009,13:933-955.
[12] T. P. Chang. Performance comparison of six numerical methods in estimating Weibullparameters for wind energy application [J]. Applied Energy,2011,88(1):272-2782.
[13] A. Ucar, F. Balo. Evaluation of wind energy potential and electricity generation at six locationsin Turkey [J]. Applied Energy,2009,86(10):1864-1872.
[14] K. Xie, R. Billinton. Energy and reliability benefits of wind energy conversion systems [J].Renewalbe Energy,2011,36(7):1983-1988.
[15]雷亚洲,王伟胜,印永华,等．基于机会约束规划的风电穿透功率极限计算[J]．中国电机工程学报,2002,22(5):32-35．
[16]雷亚洲,王伟胜,印永华,等.含风电场电力系统的有功优化潮流[J].电网技术,2002,26(6):18-21.
[17]谢开贵,王岸,胡博.计及储能设备运行策略的风/柴/储混合系统可靠性评估[J].电力系统保护与控制,2012,40(9):1-7.
[18] J. V. Seguro, T. W. Lambert. Modern estimation of the parameters of the Weibull wind speeddistribution for wind energy analysis [J]. Journal of Wind Engineering and IndustrialAerodynamics,2000,85(1):75-84.
[19]董雷,程卫东,杨以涵.含风电场的电力系统概率潮流计算[J].电网技术,2009,16(33):87-91.
[20]姚国平,余岳峰,王志征.如东沿海地区风速数据分析及风力发电量计算[J].电力自动化设备,2004,24(4):12-14.
[21] G. Bekele, B. Palm. Wind energy potential assessment at four typical locations in Ethiopia [J].Applied Energy,2009,86(3):388-96.
[22]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(10):107-110.
[23]齐雪雯,谢开贵,胡博.风速分布预测的指数平滑模型及其在配电网可靠性中的应用[J].电力系统保护与控制,2012,40(12):1-7.
[24]丁明,吴义纯,张立军.基于时间序列分析的风电场风速预测模型[J].电力系统自动化设备.2005,25(8):32-34.
[25]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): l-5.
[26] P. Chen, T. Pedersen, B. Bak-Jensen, Z. Chen. ARIMA-Based Time Series Model of StochasticWind Power Generation [J]. IEEE Transactions on on Power system.25(2):667-676.
[27] R. Billinton, H. Chen, R. Ghajar. Time-series models for reliability evaluation of powersystems including wind energy [J], Microelectronics Reliability,1996,36(9):1253-1261.
[28]孟虹年,谢开贵.计及电池储能设备运行特性的风电场可靠性评估[J].电网技术,2012,36(6):214-219.
[29] E. Ergin, J. Shi. ARMA based approaches for forecasting the tuple of wind speed and direction[J]. Applied Energy,2011,88(4):1405-1414.
[30] M. Khalid, V. S. Andrey. A method for short-term wind power prediction with multipleobservation points [J]. IEEE Transactions on Power Systems,2012,27(2):579-586.
[31]李莉,刘永前,杨勇平,等.基于CFD流场预计算的短期风速预测方法[J].中国电机工程学报.2013,33(7):27-32.
[32]李永乐,廖海黎,强士中.京沪高速铁路南京长江大桥桥址区风特性研究[J].桥梁建设,2002,4:5-7.
[33]陈隽,赵旭东.总体样本下风速风向联合分布函数的研究[C].辽宁大连,第十三届全国结构风工程学术会议论文集,2007.
[34]秦志龙,李文沅,熊小伏.考虑风速相关性的发输电系统可靠性评估[J].电力系统自动化,2013,37(16):47-52.
[35] K. Xie, Y. Li, W. Li. Modeling wind speed dependence in system reliability assessment usingcopulas [J]. IET Renewable Power Generation,2012,6(6):392-399.
[36] Y. Gao, R. Billinton. Adequacy assessment of generating systems containing wind powerconsidering wind speed correlation [J]. IET Renewable Power Generation,2009,3(2):217-226.
[37] R. Billinton. Y. Gao, R. Karki. Composite system adequacy assessment incorporatinglarge-scale wind energy conversion systems considering wind speed correlation [J]. IEEETransactions on Power Systems,2009,24(3):1375-1382.
[38] W. Wangdee, Wijarn, R. Billinton. Considering load-carrying capability and wind speedcorrelation of WECS in generation adequacy assessment [J]. IEEE Transactions on EnergyConversion,2006,21(3):734-741.
[39]范荣奇,陈金富,段献忠,等.风速相关性对概率潮流计算的影响分析[J].电力系统自动化2011,35(4):18-22.
[40] Z. Qin, W. Li, X. Xiong. Generation system reliability evaluation incorporating correlations ofwind speeds with different distributions [J]. IEEE Transactions on Power Systems,2013,28(1):551-558.
[41]潘雄,周明,孔晓民,等.风速相关性对最优潮流的影响[J].电力系统自动化,2013,37(6):37-41.
[42]秦志龙,李文沅,熊小伏.含具有风速相关性风电场的发输电系统可靠性评估[J].电力系统保护与控制,2013,41(20):27-33.
[43]季峰,蔡兴国,王俊.基于混合Copula函数的风电功率相关性分析[J].电力系统自动化,2014,1(25):1-5.
[44]杨洪明,王爽,易德鑫.考虑多风电场出力相关性的电力系统随机优化调度[J].电力自动化设备,2013,33(1):114-120.
[45]蔡菲,严正,赵静波,等.基于Copula理论的风电场间风速及输出功率相依结构建模[J].电力系统自动化,2013,37(17):9-16.
[46]黄玲玲,符杨,郭晓明.海上风电场集电系统可靠性评估[J].电网技术,2010,34(7):169-174.
[47]谭任深,杨苹,贺鹏,等.虑电气故障和开关配置方案的海上风电场集电系统可靠性及灵敏度研究[J].电网技术,2013,37(8):2264-2270.
[48]符杨,吴靖,魏书荣.大型海上风电场集电系统拓扑结构优化与规划[J].电网技术,2013,34(9):2553-2558.
[49] S. Lumbreras, and A. Ramos. Optimal design of the electrical layout of an offshore wind farmapplying decomposition strategies [J]. IEEE Transactions on Power Systems,2013,28(2):1434-1441.
[50] G. Quinonez-Varela, G. W. Ault, O. Anaya-Lara, et al. Electrical collector system options forlarge offshore wind farms [J]. IET Renewable Power Generation,2007,1(2):107-114.
[51]孙兆键,安军,李帅,等.计及风电功率波动的风电场集电系统无功电压时空分布特性分析[J].东北电力大学学报,2013,33(1):115-119.
[52] D. Sudipta, J.O.Thomas. Optimal wind farm collector system topology design consideringtotal trenching length [J]. IEEE Transactions on Sustainable Energy,2012,3(3):339-348.
[53] S. Chuangpishit, A. Tabesh, Z. Moradi-Shahrbabak, et al. Topology design for collectorsystems of offshore wind farms with pure DC power systems [J]. IEEE Transactions onIndustrial Electronics,2014,61(1):320-328.
[54] A.S. Dobakhshari, F.F. Mahmud. A reliability model of large wind farms for power systemadequacy studies [J]. IEEE Transactions on Energy Conversion,2009,24(3):792-801.
[55] R. Karki, P. Hu, R. Billinton. A simplified wind power generation model for reliabilityevaluation [J]. IEEE Transactions on Energy conversion,2006,21(2):533-540.
[56] R. Billinton, and Y. Gao. Multistate wind energy conversion system models for adequacyassessment of generating systems incorporating wind energy [J]. IEEE Transactions on EnergyConversion,2008,23(1):163-170.
[57]钟浩,唐民富.风电场发电可靠性及容量可信度评估[J].电力系统保护与控制,2012,40(18):75-80.
[58]姜文,严正,杨建林.基于解析法的风电场可靠性模型[J].电力自动化设备,2010,30(10):79-83.
[59]张宏宇,印永华,申洪,等.大规模风电接入后的系统调峰充裕性评估[J].中国电机工程学报,2011,31(22):26-31.
[60]向磊,蒋铁铮,徐晟,等.风电场对发输电系统风险评估的影响[J].水电与新能源,2013,1:66-69.
[61]吴义纯.含风电场的电力系统可靠性与规划问题的研究[D].合肥:合肥工业大学,2006.
[62]吴林伟,张建华,刘若溪.考虑风电机组故障的风电场可靠性模型及其应用[J].电力系统自动化,2012,36(16):31-35.
[63] R. Billinton, H. Chen, R. Ghajar. A sequential simulation technique for adequacy evaluation ofgenerating systems including wind energy [J]. IEEE Transactions on Energy Conversion,1996,11(4):728-734.
[64] R. Billinton, R. Karki, Y. Gao, et al. Adequacy assessment considerations in wind integratedpower systems [J]. IEEE Transactions on Power Systems,2012,27(4):2297-2305.
[65] R. Karki, P. Hu, R. Billinton. Reliability evaluation considering wind and hydro powercoordination [J]. IEEE Transactions on Power Systems,2010,25(2):685-693.
[66] P. Hu, R. Karki, R. Billinton. Reliability evaluation of generating systems containing windpower and energy storage [J]. IET Generation Transmission&Distribution,2009,3(8):783-791.
[67] R. Billinton, W. Wangdee. Reliability-based transmission reinforcement planning associatedwith large-scale wind farms [J]. IEEE Transactions on Power Systems,2007,22(1):34-41.
[68] N.B. Negra, O. Holmstrom, B.J. Birgitte, et al. Aspects of relevance in offshore wind farmreliability assessment [J]. IEEE Transactions on Energy Conversion,2007,22(1):159-166.
[69]吴义纯,丁明,李生虎.风电场对发输电系统可靠性影响的评估[J].电工技术学报,2004,19(11):72-76.
[70]王剑,李兴源,刘天琪.风电场及储能装置对发输电系统可靠性的影响[J].电网技术,2011,35(5):165-170.
[71]张硕,李庚银,周明.含风电场的发输电系统可靠性评估[J].中国电机工程学报,2010,30(7):8-14.
[72]姜文,严正.含风电场的发输电组合系统可靠性评估[J].电力自动化设备,2010,30(4):5-9.
[73]姜文,严正,杨建林.计及风电场的发输电可靠性评估[J].电力系统保护与控制,2010,38(22):126-130.
[74]刘威,赵渊,周家启,等.计及风电场的发输配电系统可靠性评估[J].电网技术,2008,32(13):69-74.
[75]张粒子,王茜.计及负荷损失费用的含风电场发输电系统可靠性评估[J].电力系统保护与控制,2010,38(20):39-44.
[76]吴昊,张焰,刘波.考虑风电场影响的发输电系统可靠性评估[J].电力系统保护与控制,2011,39(4):36-42.
[77]吴义纯,丁明.风电场可靠性评估[J].中国电力,2004,37(5):81-84.
[78]赵伟然,许昌,刘德有,等.风电场选址与风机优化排布实用技术探讨[J].电力科学与工程,2010,3:1-4
[79]田琳琳,赵宁,钟伟.基于小生境遗传算法的风电场布局优化[J].南京航空航天大学学报,2011,43(5):650-654.
[80]万春秋,王峻,杨耕,等.基于威布尔分布的风电场微观选址优化[J].太阳能学报,2011,32(7):999-1004.
[81]田琳琳,赵宁,武从海,等.复杂地形风电场的机组布局优化[J].南京航空航天大学学报,2013,45(4):503-509.
[82]万春秋,王峻,杨耕,等.动态评价粒子群优化及风电场微观选址[J].控制理论与应用,2011,28(4):449-456
[83] E. Yunus, U.S. Serap. Wind farm layout optimization using particle filtering approach [J].Renewable Energy,2013,58:95-107.
[84] B. Saavedra, S. Salcedo, A. Paniagua, et al. Seeding evolutionary algorithms with heuristicsfor optimal wind turbines positioning in wind farms [J]. Renewable Energy,2011,36(11):2838-2844.
[85] E. Yunus, S.U. Seckiner. Design of wind farm layout using ant colony algorithm [J].Renewable Energy,2012,44:53-62.
[86] K., Andrew, Z. Song. Design of wind farm layout for maximum wind energy capture [J].Renewable Energy,2010,35(3):685-694.
[87] M.X. Song, K. Chen, Z. Y. He, et al. Bionic optimization for micro-siting of wind farm oncomplex terrain [J]. Renewable Energy,2013,50:551-557.
[88] S. Turner, D. Romero, P. Zhang, et al. A new mathematical programming approach tooptimize wind farm layouts [J]. Renewable Energy,2014,63:674-680.
[89] L. Chen, E. MacDonald. A system-level cost-of-energy wind farm layout optimization withlandowner modeling [J]. Energy Conversion and Management,2014,77:484-494.
[90] J.S. Gonzalez, M.B. Payan, M. Riquelme-Santos. Optimization of wind farm turbine layoutincluding decision making under risk [J]. IEEE Systems Journal,2012,6(1):94-102.
[91] J.S. González, A.G. Rodriguezb, J.C. Morac, et al. Optimization of wind farm turbines layoutusing an evolutive algorithm [J]. Renewable Energy,2010,35(8):1671-1681.
[92] Y. Chen, H. Li, K. Jin, et al. Wind farm layout optimization using genetic algorithm withdifferent hub height wind turbines [J]. Energy Conversion and Management,2013,70:56-65.
[93] H. Walid, E. Wedad, E. Dekam. Effect of the wake behind wind rotor on optimum energyoutput of wind farms [J]. Renewable Energy,2013,49:128-132.
[94]许昌,杨建川,李辰奇,等.复杂地形风电场微观选址优化[J].中国电机工程学报,2013,33(31):58-64.
[95]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.
[96]康重庆,陈启鑫,夏清.低碳电力技术的研究展望[J].电网技术,2009,33(2):1-7.
[97]曹娜,赵海翔,任普春,等.风电场动态分析中风速模型的建立及应用[J].中国电机工程学报,2007,27(36):68-72.
[98]华文,徐政.包含风电场的发电系统可靠性研究[J].高压电器,2010,46(12):36-40.
[99]李玉敦,谢开贵,胡博.基于Copula函数的多维时序风速相依模型及其在可靠性评估中的应用[J].电网技术,2013,37(3):840-846.
[100] Y. Ma, H. Yang, X. Zhou, et al. The dynamic modeling of wind farms considering wake effectsand its optimal distribution[C]. IEEE World Non-Grid-Connected Wind Power and EnergyConference,2009:1-4.
[101]张硕,李庚银,周明,等.风电场可靠性建模[J].电网技术,2009,33(13):37-41.
[102]石文辉,陈静,王伟胜.含风电场的互联发电系统可靠性评估[J].电网技术,2012,36(2):224-230.
[103] X. Han, J. Guo, P. Wang. Adequacy study of a wind farm considering terrain and wake effect[J]. IET Generation, Transmission&Distribution,2012,6(10):1001-1008.
[104]汪海瑛,白晓民.大规模风电场的发电充裕度与容量可信度评估[J].电网技术,2012,6(36):200-206.
[105]王小平,曹立明.遗传算法:理论、应用及软件实现[M].西安交通大学出版社,2002.
[106] H. Kim, C. Singh, A. Sprintson. Simulation and estimation of reliability in a wind farmconsidering the wake effect [J]. IEEE Transactions on Sustainable Energy,2012,3(2):274-282.
[107]杨虎,刘琼荪,钟波.数理统计[M].高等教育出版社,2004.
[108] R. Billinton, A. A. Chowdhury. Incorporation of wind energy conversion systems inconventional generating capacity adequacy assessment [J]. IEE Proceedings C: GenerationTransmission and Distribution,1992,139(1):47-56.
[109] K. G. Xie, R. Billinton. Determination of the optimum capacity and type of wind turbinegenerators in a power system considering reliability and cost [J]. IEEE Transactions on EnergyConversion,2011,26(1):227-234.
[110] Y. Xu, Y. Wu. Reliability evaluation for distribution system connected with wind-turbinegenerators [J]. Power System Technology,2011,35(4):154-158.
[111] W. Li. Reliability assessment of electrical power systems using Monte Carlo methods [M].Springer,1994.
[112] R. Billinton, R.N. Alan. Reliability evaluation of power systems [M], New York: Plenum Press,1996.
[113] R. Billinton, S. Kumar, N. Chowdhury, et al. A reliability test system for educationalpurposes-basic data [J]. IEEE Transactions on Power Systems,1989,4(3):1238-1244.
[114]吴义纯,丁明.风电场风力发电机组优化选型[J].太阳能学报,2007,28(10):1163-1167.
[115]张宏宇,印永华,申洪,等.基于概率测度变换的风速时间序列建模方法[J].电力系统自动化,2013,37(2):7-10.
[116]王守相,徐群,张高磊,等.风电场风速不确定性建模及区间潮流分析[J].电力系统自动化,2009,33(21):82-86.
[117] S. Roy. Power output by active pitch-regulated wind turbine in presence of short duration windvariations [J]. IEEE Transactions on Energy Conversion,2013,28(4):2018-1025.
[118] N. O. Jensen. A note of wind generator interaction[R]. Denmark: Ris National Laboratory,1983:1-17.
[119] X. Han, Y. Qu, Peng Wang. Four-dimensional wind speed model for adequacy assessment ofpower systems with wind farms [J]. IEEE Transactions on Power Systems,2013,28(3):2978-2985.
[120] B. Nelsen. An introduction to copulas [M]. Springer:2006.
[121]韦艳华,张世英. Copula理论及其在金融分析上的应用[M].北京:清华大学出版社,2008.
[122] C. Genest. Frank’s family of bivariate distributions [J]. BIometrika,1984,74:549-555.
[123] C. Genest, L. P. Rivest. Stasitcal inference procedures for bivariate Archimedean Copulas [J].Journal of the American Statistical Associateion,1993,88:1034-1043.
[124] E. W. Frees, E. A. Valdez. Understanding relationships using Copuls [J]. North AmericanActuarial Journal,1998,2(1):1-25.
[125]盛骤,谢式千,潘承毅.概率论和数理统计[M].北京:高等教育出版社,2001.
[126] Royal Netherlands Meteorological Institute. Potential wind speed of the Netherlands [EB/OL].http://www.knmi.nl.
[127] T. Hammons. Renewable energy [M]. Vukovar: In-Tech,2009.
[128] S. Muyeen. Wind power [M]. Vukovar: In-Tech,2010.
[129] K.G. Xie, Z.F. Jiang, W.Y. Li. Effect of wind speed on wind turbine power converter reliability[J]. IEEE Transactions on Energy Conversion,2012,27(1):96-104.
[130] A. Thomas. Transmission systems for offshore wind farms [J]. IEEE Power EngineeringReview,2002,22(12):23-27.
[131] J. Ribrant, L. Bertling. Survey of failures in wind power systems with focus on Swedish windpower plants during1997-2005[C]. IEEE Power Engineering Society General Meeting, PES,2007.
[132] E. H. Camm, M. R. Behnke, O. Bolado, et al. Wind power plant substation and collectorsystem redundancy, reliability, and economics [C]. IEEE Power and Energy Society GeneralMeeting, PES,2009.
[133] E. H. Camm, M. R. Behnke, O. Bolado, et al. Wind power plant collector system designconsiderations[C]. IEEE Power and Energy Society General Meeting, PES,2009.
[134] H. Ole, B. N. Nicola. Survey of Reliability of Large Offshore Wind Farms [C]. European WindEnergy Conference,2007.
[135] A. Sannino, H. Breder, E. K. Nielsen. Reliability of collection grids for large offshore windparks [C].9th International Conference on Probabilistic Methods Applied to Power Systems,2006:1-6.
[136] M. Scutariu. Techno-economical optioneering of offshore wind farms electrical systems [J].2007IEEE Lausanne PowerTech,2007,1(5):2195-2200.
[137] E. Muljadi, C. P. Butterfield, A. Ellis, et al. Equivalencing the collector system of a large windpower plant[C]. IEEE Power Engineering Society General Meeting, PES,2006.
[138] R. Billinton, Y. F. Li. Incorporating multi-state unit models in composite system adequacyassessment [J]. European Transactions on Electrical Power,2007,17(4):375-386.
[139] K. G. Xie, K. Cao, P. Wang. Optimal placement of switching devices in power distributionnetworks using the switch-position-exchange technique [J]. International Review of ElectricalEngineering (IREE),2011,6(3):1349-1355.
[140] J. Yang, J. O. Reilly, J. E. Fletcher. Redundancy analysis of offshore wind farm collection andtransmission systems [C].1st International Conference on Sustainable Power Generation andSupply, Supergen,2009.
[141] M. S. Kang, C. S. Chen, Y. L. Ke, et al. Applications of FANP and BOCR in renewableenergy-study on the choice of the sites for wind farms [J]. IEEE Transactions on IndustryApplications,2013,49(2):982-989.
[142] V. S. Pappala, I. Erlich, K. Rohrig, et al. A stochastic model for the optimal operation of awind-thermal power system [J]. IEEE Transactions on Power Systems,2009,24(2):940-950.
[143] M. Adaramola, P. Krogstad. Experimental investigation of wake-effects on wind turbineperformance [J]. Renewable Energy,2011,36(8):2078-2086.
[144] G. Marmidis, S. Lazarou, E. Pyrgioti. Optimal placement of wind turbines in a wind park usingMonte Carlo simulation [J]. Renewable Energy,2008,33(7):1455-1460.
[145] S.A. Grady, M.Y. Hussaini, M.M. Abdullah. Placement of wind turbines using geneticalgorithms [J]. Renewable Energy,2005,30(2):259-270.
[146] S. Pookpunt, W. Ongsakul. Optimal placement of wind turbines within wind farm using binaryparticle swarm optimization with time-varying acceleration coefficients [J], Renewable Energy,2013,55:266-276.
[147] X. Han, J. Guo, P. Wang, et al. Adequacy study of wind farms considering reliability andwake-effect of WTGs [J]. IEEE Power and Energy Society General Meeting,2011:1-7.
[148]曾建潮,介婧,崔志华.微粒群算法[M].科学出版社,2004.
[149] Y. Shi, R.C. Eberhart. Parameter selection in particle swarm optimization [J]. EvolutionaryProgramming VII,1998,1447:591-600.
[150]李宁,刘飞,孙德宝.基于带变异算子粒子群优化算法的约束布局优化研究[J].计算机学报,2004,27(7):897-903.
[151] Y. Shi, R.C. Eberhart. Empirical study of particle swarm optimization [C]. Proceedings of the1999Congress on Evolutionary Computatio,1999,3:1945-1950.
[152] C. Maurice, Particle Swarm Optimization [M], Wiltshire: Wiley-ISTE,2006.
[153] M. Antonios, J. Pan, M. Reza, et al. Investigating the impact of wake-effect on wind farmaggregation [J]. IEEE Trondheim PowerTech,2011:1-5.
[154] M. Singh, A. Chandra. Application of adaptive network-based fuzzy inference system forsensorless control of PMSG-based wind turbine with nonlinear-load-compensation capabilities[J]. IEEE Transactions on Power Electronics,2011,26(1):165-175.
[155] N. Maisonneuve, G. Gross. A production simulation tool for systems with integrated windenergy resources [J]. IEEE Transactions on Power Systems,2011,26(4):2285-2292.
[156] T. Zhou, B. Fran ois. Energy management and power control of a hybrid active windgenerator for distributed power generation and grid integration [J]. IEEE Transactions onIndustrial Electronics,2011,58(1):95-104.
[157] M.C. Alexiadis, P.S. Dokopoulos, H.S. Sahsamanoglou. Wind speed and power forecastingbased on spatial correlation models [J]. IEEE Transactions on Energy Conversion,1999,14(3):836-842.
[158] N. Amjady. Generation adequacy assessment of power systems by time series and fuzzy neuralnetwork [J]. IEEE Transactions on Power System,2006,21(3):1340-1349.
[159] N. Samaan, C. Singh. Adequacy assessment of power system generation using a modifiedsimple genetic algorithm [J]. IEEE Transaction on Power Systems,2002,17(4):974-981.
[160]赵渊,吴小平,谢开贵.基于频率动态特性的电力系统频率失稳概率评估[J].电工技术学报,2012,27(5):212-220.
[161] P. Wang, Z. Gao, L.B. Tjernberg. Operational adequacy studies of power systems with windfarms and energy storages [J]. IEEE Transactions on Power Systems,2012,27(4):2377-2384.
[162] T. Ackermann. Wind power in power systems [M]. UK: John Wiley,2005.
[163] J. Machowski, J. W. Bialek, J. R. Bumby. Power system dynamics: stability and control [M],Wiley,2008.
[164] R. Billinton, S. Kumar, N. Chowdhury, et al. Reliability test system for educationalpurposes-basic data [J]. IEEE Transactions on Power Systems,1989,4(3):1238-1244.
[2] Global Wind Energy Council (GWEC). Global wind report annual market update [EB/OL].http://www.gwec.net,2012.
[3]李俊伟,蔡丰波,乔黎明,等.2013中国风电发展报告[R].2013.
[4]周家启、蒋锦峰、孙渝江、谢开贵.电力可靠性基本名词术语[S].北京：中国电力出版社.
[5]朱永强,张旭.风电场电气系统[M].北京:机械工业出版社,2010.
[6]李玉敦.计及相关性的风速模型及其在发电系统可靠性评估中的应用[D].重庆:重庆大学,2012.
[7]姚兴佳,宋俊.机械工业出版社风力发电机组原理与应用[M].北京:机械工业出版社,2009.
[8]国家电力监管委员会电力可靠性管理中心.电力可靠性技术与管理培训教材[M].北京：中国电力出版社,2007.
[9]郭永基.电力系统可靠性分析[M].北京：清华大学出版社,2003.
[10]李文沅.电力系统风险评估：模型,方法和应用[M].北京：科学出版社,2006.
[11] J. A. Carta, P. Ramirez, S. Velazquez. A review of wind speed probability distributions used inwind energy analysis: case studies in the Canary Islands [J]. Renewable and SustainableEnergy Reviews,2009,13:933-955.
[12] T. P. Chang. Performance comparison of six numerical methods in estimating Weibullparameters for wind energy application [J]. Applied Energy,2011,88(1):272-2782.
[13] A. Ucar, F. Balo. Evaluation of wind energy potential and electricity generation at six locationsin Turkey [J]. Applied Energy,2009,86(10):1864-1872.
[14] K. Xie, R. Billinton. Energy and reliability benefits of wind energy conversion systems [J].Renewalbe Energy,2011,36(7):1983-1988.
[15]雷亚洲,王伟胜,印永华,等．基于机会约束规划的风电穿透功率极限计算[J]．中国电机工程学报,2002,22(5):32-35．
[16]雷亚洲,王伟胜,印永华,等.含风电场电力系统的有功优化潮流[J].电网技术,2002,26(6):18-21.
[17]谢开贵,王岸,胡博.计及储能设备运行策略的风/柴/储混合系统可靠性评估[J].电力系统保护与控制,2012,40(9):1-7.
[18] J. V. Seguro, T. W. Lambert. Modern estimation of the parameters of the Weibull wind speeddistribution for wind energy analysis [J]. Journal of Wind Engineering and IndustrialAerodynamics,2000,85(1):75-84.
[19]董雷,程卫东,杨以涵.含风电场的电力系统概率潮流计算[J].电网技术,2009,16(33):87-91.
[20]姚国平,余岳峰,王志征.如东沿海地区风速数据分析及风力发电量计算[J].电力自动化设备,2004,24(4):12-14.
[21] G. Bekele, B. Palm. Wind energy potential assessment at four typical locations in Ethiopia [J].Applied Energy,2009,86(3):388-96.
[22]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(10):107-110.
[23]齐雪雯,谢开贵,胡博.风速分布预测的指数平滑模型及其在配电网可靠性中的应用[J].电力系统保护与控制,2012,40(12):1-7.
[24]丁明,吴义纯,张立军.基于时间序列分析的风电场风速预测模型[J].电力系统自动化设备.2005,25(8):32-34.
[25]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): l-5.
[26] P. Chen, T. Pedersen, B. Bak-Jensen, Z. Chen. ARIMA-Based Time Series Model of StochasticWind Power Generation [J]. IEEE Transactions on on Power system.25(2):667-676.
[27] R. Billinton, H. Chen, R. Ghajar. Time-series models for reliability evaluation of powersystems including wind energy [J], Microelectronics Reliability,1996,36(9):1253-1261.
[28]孟虹年,谢开贵.计及电池储能设备运行特性的风电场可靠性评估[J].电网技术,2012,36(6):214-219.
[29] E. Ergin, J. Shi. ARMA based approaches for forecasting the tuple of wind speed and direction[J]. Applied Energy,2011,88(4):1405-1414.
[30] M. Khalid, V. S. Andrey. A method for short-term wind power prediction with multipleobservation points [J]. IEEE Transactions on Power Systems,2012,27(2):579-586.
[31]李莉,刘永前,杨勇平,等.基于CFD流场预计算的短期风速预测方法[J].中国电机工程学报.2013,33(7):27-32.
[32]李永乐,廖海黎,强士中.京沪高速铁路南京长江大桥桥址区风特性研究[J].桥梁建设,2002,4:5-7.
[33]陈隽,赵旭东.总体样本下风速风向联合分布函数的研究[C].辽宁大连,第十三届全国结构风工程学术会议论文集,2007.
[34]秦志龙,李文沅,熊小伏.考虑风速相关性的发输电系统可靠性评估[J].电力系统自动化,2013,37(16):47-52.
[35] K. Xie, Y. Li, W. Li. Modeling wind speed dependence in system reliability assessment usingcopulas [J]. IET Renewable Power Generation,2012,6(6):392-399.
[36] Y. Gao, R. Billinton. Adequacy assessment of generating systems containing wind powerconsidering wind speed correlation [J]. IET Renewable Power Generation,2009,3(2):217-226.
[37] R. Billinton. Y. Gao, R. Karki. Composite system adequacy assessment incorporatinglarge-scale wind energy conversion systems considering wind speed correlation [J]. IEEETransactions on Power Systems,2009,24(3):1375-1382.
[38] W. Wangdee, Wijarn, R. Billinton. Considering load-carrying capability and wind speedcorrelation of WECS in generation adequacy assessment [J]. IEEE Transactions on EnergyConversion,2006,21(3):734-741.
[39]范荣奇,陈金富,段献忠,等.风速相关性对概率潮流计算的影响分析[J].电力系统自动化2011,35(4):18-22.
[40] Z. Qin, W. Li, X. Xiong. Generation system reliability evaluation incorporating correlations ofwind speeds with different distributions [J]. IEEE Transactions on Power Systems,2013,28(1):551-558.
[41]潘雄,周明,孔晓民,等.风速相关性对最优潮流的影响[J].电力系统自动化,2013,37(6):37-41.
[42]秦志龙,李文沅,熊小伏.含具有风速相关性风电场的发输电系统可靠性评估[J].电力系统保护与控制,2013,41(20):27-33.
[43]季峰,蔡兴国,王俊.基于混合Copula函数的风电功率相关性分析[J].电力系统自动化,2014,1(25):1-5.
[44]杨洪明,王爽,易德鑫.考虑多风电场出力相关性的电力系统随机优化调度[J].电力自动化设备,2013,33(1):114-120.
[45]蔡菲,严正,赵静波,等.基于Copula理论的风电场间风速及输出功率相依结构建模[J].电力系统自动化,2013,37(17):9-16.
[46]黄玲玲,符杨,郭晓明.海上风电场集电系统可靠性评估[J].电网技术,2010,34(7):169-174.
[47]谭任深,杨苹,贺鹏,等.虑电气故障和开关配置方案的海上风电场集电系统可靠性及灵敏度研究[J].电网技术,2013,37(8):2264-2270.
[48]符杨,吴靖,魏书荣.大型海上风电场集电系统拓扑结构优化与规划[J].电网技术,2013,34(9):2553-2558.
[49] S. Lumbreras, and A. Ramos. Optimal design of the electrical layout of an offshore wind farmapplying decomposition strategies [J]. IEEE Transactions on Power Systems,2013,28(2):1434-1441.
[50] G. Quinonez-Varela, G. W. Ault, O. Anaya-Lara, et al. Electrical collector system options forlarge offshore wind farms [J]. IET Renewable Power Generation,2007,1(2):107-114.
[51]孙兆键,安军,李帅,等.计及风电功率波动的风电场集电系统无功电压时空分布特性分析[J].东北电力大学学报,2013,33(1):115-119.
[52] D. Sudipta, J.O.Thomas. Optimal wind farm collector system topology design consideringtotal trenching length [J]. IEEE Transactions on Sustainable Energy,2012,3(3):339-348.
[53] S. Chuangpishit, A. Tabesh, Z. Moradi-Shahrbabak, et al. Topology design for collectorsystems of offshore wind farms with pure DC power systems [J]. IEEE Transactions onIndustrial Electronics,2014,61(1):320-328.
[54] A.S. Dobakhshari, F.F. Mahmud. A reliability model of large wind farms for power systemadequacy studies [J]. IEEE Transactions on Energy Conversion,2009,24(3):792-801.
[55] R. Karki, P. Hu, R. Billinton. A simplified wind power generation model for reliabilityevaluation [J]. IEEE Transactions on Energy conversion,2006,21(2):533-540.
[56] R. Billinton, and Y. Gao. Multistate wind energy conversion system models for adequacyassessment of generating systems incorporating wind energy [J]. IEEE Transactions on EnergyConversion,2008,23(1):163-170.
[57]钟浩,唐民富.风电场发电可靠性及容量可信度评估[J].电力系统保护与控制,2012,40(18):75-80.
[58]姜文,严正,杨建林.基于解析法的风电场可靠性模型[J].电力自动化设备,2010,30(10):79-83.
[59]张宏宇,印永华,申洪,等.大规模风电接入后的系统调峰充裕性评估[J].中国电机工程学报,2011,31(22):26-31.
[60]向磊,蒋铁铮,徐晟,等.风电场对发输电系统风险评估的影响[J].水电与新能源,2013,1:66-69.
[61]吴义纯.含风电场的电力系统可靠性与规划问题的研究[D].合肥:合肥工业大学,2006.
[62]吴林伟,张建华,刘若溪.考虑风电机组故障的风电场可靠性模型及其应用[J].电力系统自动化,2012,36(16):31-35.
[63] R. Billinton, H. Chen, R. Ghajar. A sequential simulation technique for adequacy evaluation ofgenerating systems including wind energy [J]. IEEE Transactions on Energy Conversion,1996,11(4):728-734.
[64] R. Billinton, R. Karki, Y. Gao, et al. Adequacy assessment considerations in wind integratedpower systems [J]. IEEE Transactions on Power Systems,2012,27(4):2297-2305.
[65] R. Karki, P. Hu, R. Billinton. Reliability evaluation considering wind and hydro powercoordination [J]. IEEE Transactions on Power Systems,2010,25(2):685-693.
[66] P. Hu, R. Karki, R. Billinton. Reliability evaluation of generating systems containing windpower and energy storage [J]. IET Generation Transmission&Distribution,2009,3(8):783-791.
[67] R. Billinton, W. Wangdee. Reliability-based transmission reinforcement planning associatedwith large-scale wind farms [J]. IEEE Transactions on Power Systems,2007,22(1):34-41.
[68] N.B. Negra, O. Holmstrom, B.J. Birgitte, et al. Aspects of relevance in offshore wind farmreliability assessment [J]. IEEE Transactions on Energy Conversion,2007,22(1):159-166.
[69]吴义纯,丁明,李生虎.风电场对发输电系统可靠性影响的评估[J].电工技术学报,2004,19(11):72-76.
[70]王剑,李兴源,刘天琪.风电场及储能装置对发输电系统可靠性的影响[J].电网技术,2011,35(5):165-170.
[71]张硕,李庚银,周明.含风电场的发输电系统可靠性评估[J].中国电机工程学报,2010,30(7):8-14.
[72]姜文,严正.含风电场的发输电组合系统可靠性评估[J].电力自动化设备,2010,30(4):5-9.
[73]姜文,严正,杨建林.计及风电场的发输电可靠性评估[J].电力系统保护与控制,2010,38(22):126-130.
[74]刘威,赵渊,周家启,等.计及风电场的发输配电系统可靠性评估[J].电网技术,2008,32(13):69-74.
[75]张粒子,王茜.计及负荷损失费用的含风电场发输电系统可靠性评估[J].电力系统保护与控制,2010,38(20):39-44.
[76]吴昊,张焰,刘波.考虑风电场影响的发输电系统可靠性评估[J].电力系统保护与控制,2011,39(4):36-42.
[77]吴义纯,丁明.风电场可靠性评估[J].中国电力,2004,37(5):81-84.
[78]赵伟然,许昌,刘德有,等.风电场选址与风机优化排布实用技术探讨[J].电力科学与工程,2010,3:1-4
[79]田琳琳,赵宁,钟伟.基于小生境遗传算法的风电场布局优化[J].南京航空航天大学学报,2011,43(5):650-654.
[80]万春秋,王峻,杨耕,等.基于威布尔分布的风电场微观选址优化[J].太阳能学报,2011,32(7):999-1004.
[81]田琳琳,赵宁,武从海,等.复杂地形风电场的机组布局优化[J].南京航空航天大学学报,2013,45(4):503-509.
[82]万春秋,王峻,杨耕,等.动态评价粒子群优化及风电场微观选址[J].控制理论与应用,2011,28(4):449-456
[83] E. Yunus, U.S. Serap. Wind farm layout optimization using particle filtering approach [J].Renewable Energy,2013,58:95-107.
[84] B. Saavedra, S. Salcedo, A. Paniagua, et al. Seeding evolutionary algorithms with heuristicsfor optimal wind turbines positioning in wind farms [J]. Renewable Energy,2011,36(11):2838-2844.
[85] E. Yunus, S.U. Seckiner. Design of wind farm layout using ant colony algorithm [J].Renewable Energy,2012,44:53-62.
[86] K., Andrew, Z. Song. Design of wind farm layout for maximum wind energy capture [J].Renewable Energy,2010,35(3):685-694.
[87] M.X. Song, K. Chen, Z. Y. He, et al. Bionic optimization for micro-siting of wind farm oncomplex terrain [J]. Renewable Energy,2013,50:551-557.
[88] S. Turner, D. Romero, P. Zhang, et al. A new mathematical programming approach tooptimize wind farm layouts [J]. Renewable Energy,2014,63:674-680.
[89] L. Chen, E. MacDonald. A system-level cost-of-energy wind farm layout optimization withlandowner modeling [J]. Energy Conversion and Management,2014,77:484-494.
[90] J.S. Gonzalez, M.B. Payan, M. Riquelme-Santos. Optimization of wind farm turbine layoutincluding decision making under risk [J]. IEEE Systems Journal,2012,6(1):94-102.
[91] J.S. González, A.G. Rodriguezb, J.C. Morac, et al. Optimization of wind farm turbines layoutusing an evolutive algorithm [J]. Renewable Energy,2010,35(8):1671-1681.
[92] Y. Chen, H. Li, K. Jin, et al. Wind farm layout optimization using genetic algorithm withdifferent hub height wind turbines [J]. Energy Conversion and Management,2013,70:56-65.
[93] H. Walid, E. Wedad, E. Dekam. Effect of the wake behind wind rotor on optimum energyoutput of wind farms [J]. Renewable Energy,2013,49:128-132.
[94]许昌,杨建川,李辰奇,等.复杂地形风电场微观选址优化[J].中国电机工程学报,2013,33(31):58-64.
[95]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.
[96]康重庆,陈启鑫,夏清.低碳电力技术的研究展望[J].电网技术,2009,33(2):1-7.
[97]曹娜,赵海翔,任普春,等.风电场动态分析中风速模型的建立及应用[J].中国电机工程学报,2007,27(36):68-72.
[98]华文,徐政.包含风电场的发电系统可靠性研究[J].高压电器,2010,46(12):36-40.
[99]李玉敦,谢开贵,胡博.基于Copula函数的多维时序风速相依模型及其在可靠性评估中的应用[J].电网技术,2013,37(3):840-846.
[100] Y. Ma, H. Yang, X. Zhou, et al. The dynamic modeling of wind farms considering wake effectsand its optimal distribution[C]. IEEE World Non-Grid-Connected Wind Power and EnergyConference,2009:1-4.
[101]张硕,李庚银,周明,等.风电场可靠性建模[J].电网技术,2009,33(13):37-41.
[102]石文辉,陈静,王伟胜.含风电场的互联发电系统可靠性评估[J].电网技术,2012,36(2):224-230.
[103] X. Han, J. Guo, P. Wang. Adequacy study of a wind farm considering terrain and wake effect[J]. IET Generation, Transmission&Distribution,2012,6(10):1001-1008.
[104]汪海瑛,白晓民.大规模风电场的发电充裕度与容量可信度评估[J].电网技术,2012,6(36):200-206.
[105]王小平,曹立明.遗传算法:理论、应用及软件实现[M].西安交通大学出版社,2002.
[106] H. Kim, C. Singh, A. Sprintson. Simulation and estimation of reliability in a wind farmconsidering the wake effect [J]. IEEE Transactions on Sustainable Energy,2012,3(2):274-282.
[107]杨虎,刘琼荪,钟波.数理统计[M].高等教育出版社,2004.
[108] R. Billinton, A. A. Chowdhury. Incorporation of wind energy conversion systems inconventional generating capacity adequacy assessment [J]. IEE Proceedings C: GenerationTransmission and Distribution,1992,139(1):47-56.
[109] K. G. Xie, R. Billinton. Determination of the optimum capacity and type of wind turbinegenerators in a power system considering reliability and cost [J]. IEEE Transactions on EnergyConversion,2011,26(1):227-234.
[110] Y. Xu, Y. Wu. Reliability evaluation for distribution system connected with wind-turbinegenerators [J]. Power System Technology,2011,35(4):154-158.
[111] W. Li. Reliability assessment of electrical power systems using Monte Carlo methods [M].Springer,1994.
[112] R. Billinton, R.N. Alan. Reliability evaluation of power systems [M], New York: Plenum Press,1996.
[113] R. Billinton, S. Kumar, N. Chowdhury, et al. A reliability test system for educationalpurposes-basic data [J]. IEEE Transactions on Power Systems,1989,4(3):1238-1244.
[114]吴义纯,丁明.风电场风力发电机组优化选型[J].太阳能学报,2007,28(10):1163-1167.
[115]张宏宇,印永华,申洪,等.基于概率测度变换的风速时间序列建模方法[J].电力系统自动化,2013,37(2):7-10.
[116]王守相,徐群,张高磊,等.风电场风速不确定性建模及区间潮流分析[J].电力系统自动化,2009,33(21):82-86.
[117] S. Roy. Power output by active pitch-regulated wind turbine in presence of short duration windvariations [J]. IEEE Transactions on Energy Conversion,2013,28(4):2018-1025.
[118] N. O. Jensen. A note of wind generator interaction[R]. Denmark: Ris National Laboratory,1983:1-17.
[119] X. Han, Y. Qu, Peng Wang. Four-dimensional wind speed model for adequacy assessment ofpower systems with wind farms [J]. IEEE Transactions on Power Systems,2013,28(3):2978-2985.
[120] B. Nelsen. An introduction to copulas [M]. Springer:2006.
[121]韦艳华,张世英. Copula理论及其在金融分析上的应用[M].北京:清华大学出版社,2008.
[122] C. Genest. Frank’s family of bivariate distributions [J]. BIometrika,1984,74:549-555.
[123] C. Genest, L. P. Rivest. Stasitcal inference procedures for bivariate Archimedean Copulas [J].Journal of the American Statistical Associateion,1993,88:1034-1043.
[124] E. W. Frees, E. A. Valdez. Understanding relationships using Copuls [J]. North AmericanActuarial Journal,1998,2(1):1-25.
[125]盛骤,谢式千,潘承毅.概率论和数理统计[M].北京:高等教育出版社,2001.
[126] Royal Netherlands Meteorological Institute. Potential wind speed of the Netherlands [EB/OL].http://www.knmi.nl.
[127] T. Hammons. Renewable energy [M]. Vukovar: In-Tech,2009.
[128] S. Muyeen. Wind power [M]. Vukovar: In-Tech,2010.
[129] K.G. Xie, Z.F. Jiang, W.Y. Li. Effect of wind speed on wind turbine power converter reliability[J]. IEEE Transactions on Energy Conversion,2012,27(1):96-104.
[130] A. Thomas. Transmission systems for offshore wind farms [J]. IEEE Power EngineeringReview,2002,22(12):23-27.
[131] J. Ribrant, L. Bertling. Survey of failures in wind power systems with focus on Swedish windpower plants during1997-2005[C]. IEEE Power Engineering Society General Meeting, PES,2007.
[132] E. H. Camm, M. R. Behnke, O. Bolado, et al. Wind power plant substation and collectorsystem redundancy, reliability, and economics [C]. IEEE Power and Energy Society GeneralMeeting, PES,2009.
[133] E. H. Camm, M. R. Behnke, O. Bolado, et al. Wind power plant collector system designconsiderations[C]. IEEE Power and Energy Society General Meeting, PES,2009.
[134] H. Ole, B. N. Nicola. Survey of Reliability of Large Offshore Wind Farms [C]. European WindEnergy Conference,2007.
[135] A. Sannino, H. Breder, E. K. Nielsen. Reliability of collection grids for large offshore windparks [C].9th International Conference on Probabilistic Methods Applied to Power Systems,2006:1-6.
[136] M. Scutariu. Techno-economical optioneering of offshore wind farms electrical systems [J].2007IEEE Lausanne PowerTech,2007,1(5):2195-2200.
[137] E. Muljadi, C. P. Butterfield, A. Ellis, et al. Equivalencing the collector system of a large windpower plant[C]. IEEE Power Engineering Society General Meeting, PES,2006.
[138] R. Billinton, Y. F. Li. Incorporating multi-state unit models in composite system adequacyassessment [J]. European Transactions on Electrical Power,2007,17(4):375-386.
[139] K. G. Xie, K. Cao, P. Wang. Optimal placement of switching devices in power distributionnetworks using the switch-position-exchange technique [J]. International Review of ElectricalEngineering (IREE),2011,6(3):1349-1355.
[140] J. Yang, J. O. Reilly, J. E. Fletcher. Redundancy analysis of offshore wind farm collection andtransmission systems [C].1st International Conference on Sustainable Power Generation andSupply, Supergen,2009.
[141] M. S. Kang, C. S. Chen, Y. L. Ke, et al. Applications of FANP and BOCR in renewableenergy-study on the choice of the sites for wind farms [J]. IEEE Transactions on IndustryApplications,2013,49(2):982-989.
[142] V. S. Pappala, I. Erlich, K. Rohrig, et al. A stochastic model for the optimal operation of awind-thermal power system [J]. IEEE Transactions on Power Systems,2009,24(2):940-950.
[143] M. Adaramola, P. Krogstad. Experimental investigation of wake-effects on wind turbineperformance [J]. Renewable Energy,2011,36(8):2078-2086.
[144] G. Marmidis, S. Lazarou, E. Pyrgioti. Optimal placement of wind turbines in a wind park usingMonte Carlo simulation [J]. Renewable Energy,2008,33(7):1455-1460.
[145] S.A. Grady, M.Y. Hussaini, M.M. Abdullah. Placement of wind turbines using geneticalgorithms [J]. Renewable Energy,2005,30(2):259-270.
[146] S. Pookpunt, W. Ongsakul. Optimal placement of wind turbines within wind farm using binaryparticle swarm optimization with time-varying acceleration coefficients [J], Renewable Energy,2013,55:266-276.
[147] X. Han, J. Guo, P. Wang, et al. Adequacy study of wind farms considering reliability andwake-effect of WTGs [J]. IEEE Power and Energy Society General Meeting,2011:1-7.
[148]曾建潮,介婧,崔志华.微粒群算法[M].科学出版社,2004.
[149] Y. Shi, R.C. Eberhart. Parameter selection in particle swarm optimization [J]. EvolutionaryProgramming VII,1998,1447:591-600.
[150]李宁,刘飞,孙德宝.基于带变异算子粒子群优化算法的约束布局优化研究[J].计算机学报,2004,27(7):897-903.
[151] Y. Shi, R.C. Eberhart. Empirical study of particle swarm optimization [C]. Proceedings of the1999Congress on Evolutionary Computatio,1999,3:1945-1950.
[152] C. Maurice, Particle Swarm Optimization [M], Wiltshire: Wiley-ISTE,2006.
[153] M. Antonios, J. Pan, M. Reza, et al. Investigating the impact of wake-effect on wind farmaggregation [J]. IEEE Trondheim PowerTech,2011:1-5.
[154] M. Singh, A. Chandra. Application of adaptive network-based fuzzy inference system forsensorless control of PMSG-based wind turbine with nonlinear-load-compensation capabilities[J]. IEEE Transactions on Power Electronics,2011,26(1):165-175.
[155] N. Maisonneuve, G. Gross. A production simulation tool for systems with integrated windenergy resources [J]. IEEE Transactions on Power Systems,2011,26(4):2285-2292.
[156] T. Zhou, B. Fran ois. Energy management and power control of a hybrid active windgenerator for distributed power generation and grid integration [J]. IEEE Transactions onIndustrial Electronics,2011,58(1):95-104.
[157] M.C. Alexiadis, P.S. Dokopoulos, H.S. Sahsamanoglou. Wind speed and power forecastingbased on spatial correlation models [J]. IEEE Transactions on Energy Conversion,1999,14(3):836-842.
[158] N. Amjady. Generation adequacy assessment of power systems by time series and fuzzy neuralnetwork [J]. IEEE Transactions on Power System,2006,21(3):1340-1349.
[159] N. Samaan, C. Singh. Adequacy assessment of power system generation using a modifiedsimple genetic algorithm [J]. IEEE Transaction on Power Systems,2002,17(4):974-981.
[160]赵渊,吴小平,谢开贵.基于频率动态特性的电力系统频率失稳概率评估[J].电工技术学报,2012,27(5):212-220.
[161] P. Wang, Z. Gao, L.B. Tjernberg. Operational adequacy studies of power systems with windfarms and energy storages [J]. IEEE Transactions on Power Systems,2012,27(4):2377-2384.
[162] T. Ackermann. Wind power in power systems [M]. UK: John Wiley,2005.
[163] J. Machowski, J. W. Bialek, J. R. Bumby. Power system dynamics: stability and control [M],Wiley,2008.
[164] R. Billinton, S. Kumar, N. Chowdhury, et al. Reliability test system for educationalpurposes-basic data [J]. IEEE Transactions on Power Systems,1989,4(3):1238-1244.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |