架空输电线路输电能力的研究
|
摘要
随着节能减排政策的提出,能源使用的洁净化迫在眉睫,电源得到了快速的发展,风电作为一种清洁和可再生能源得到了很好的重视,越来越多的风电场直接并入输电系统,风电场输出功率对风速有较大的依赖性,其固有的波动性和不可控性必然对电网的输电能力产生影响,而且随着经济和社会的发展,负荷不断攀升,电网输电能力呈现不足,因此,在现有电网运行条件下,如何准确计算架空输电线路的输电能力,充分挖掘架空输电线路潜在的能力,提高现有电网的使用效率,缓解输电能力不足,已成为学术界和工程界共同关注的焦点。
目前,动态热定值(dynamic thermal rating, DTR)技术已较为成熟,DTR技术通过输电线路上增加各种测量、通信设备,实现对输电线路运行温度、热定值的实时跟踪和计算,有效挖掘了输电线路的输电潜力,输电线路动态热定值和大型风电场并网对架空输电线路输电能力的研究,无论从深度和广度上都提出若干新的问题,如：DTR技术硬件的依赖性及其运行维护的昂贵费用,使其应用难以普及的问题；DTR热定值基础上,综合考虑电压水平和功角静稳等电力系统运行限制的运行条件下输电线路载荷能力定值问题；风速变化是随机的,风速及风功率的准确预测问题；大型风电场并网,风电场输出功率的波动影响电网输电能力的问题等,为此,本文在动态热定值环境下,及风电场并网背景下,对架空输电线路输电能力进行了深入的研究。研究的主要内容和成果如下：
(1)针对DTR技术对硬件的依赖性及其运行维护的昂贵费用,给出了在仅需测量输电线路载流和导线温度的情况下实现DTR功能的方法。首先,对IEEE标准的输电线路热平衡方程各项系数的计算公式及其相关的物理意义进行了描述,在此基础上,将输电线路热平衡方程离散化,将风向、风速、日照、环境温度等综合影响通过输电线路热平衡方程离散参数来进行等效；其次,利用限定记忆广义最小二乘递推估计方法实现热平衡方程离散参数的在线估计,该方法克服了经典最小二乘递推法的增益矩阵随时间推移渐趋于零而导致的修正能力降低问题,获得了热平衡方程离散参数的一致、无偏估计。在采样时间较短的连续采样过程中,通过递推过程实现了热平衡方程时变离散参数的近似连续辨识,达到了对输电线路热平衡方程在线建模的目的；再次,在输电线路热平衡方程离散参数已知的情况下,方便地实现了输电线路长期和短期热载荷能力的实时在线定值；最后,通过算例分析表明基于输电线路热平衡方程离散参数的输电线路载流量在线定值简化了气象条件量测系统,只需测量输电线路载流量和导线温度即可实现输电线路长期和短期最大允许载流量实时在线定值,从而有效挖掘输电线路的输电潜力。
(2)针对运行条件下的输电线路载荷能力除了受热限制外,还受到电压水平和功角静稳等电力系统运行限制,在DTR热定值基础上,建立了运行条件下交流输电线路载流量在线定值模型和算法。首先,通过简化的输电线路送、受端系统等值模型,对线路运行的系统实时状态对线路载荷能力的影响进行了定性分析,其具体体现在分析不同电压等级下,输电线路送受端系统简化等值参数对线路载流量的影响,从而说明了建立实时状态下系统等值模型在线分析输电线路载荷能力的必要性；其次,通过系统节点阻抗矩阵的变换和等值化简,推导出建立考虑系统的整体性,体现并行流对输电线路载荷能力影响的送受端系统双端电源等值模型。该模型推导过程简单,且物理意义明确；最后,基于状态估计,实现在线跟踪双端电源等值参数,并综合考虑热电流、电压和静态稳定限制,实现运行条件下输电线路载荷能力在线定值计算,通过山东电网220kV输电线路载流量在线定值的计算验证了模型和计算方法的有效性。
(3)精确的风速预测,有助于风电场风功率预测的精度提高,有效地减轻或避免风电并网带来的不利影响。首先,在不需要大量的历史数据资料情况下,采用新一代中尺度非静力WRF预报模式进行风速预测,利用WRF模式提供的高分辨率优先级设计,有效定位风电场地理位置,捕获天气系统的特征,利用不同物理方案的选择和先进的资料同化系统,建立多种风速预测模型方案；其次,针对各种不同的方案分别进行预测时效为12小时和24小时的风速预测,最后,与由山东安成风电场SCADA系统获得的风电场实测风速数据相比较,通过误差分析验证基于WRF模式的风速预测是提高预测精度的新的有效方法。本文基于WRF模式风速预测以全方位的展示与设计视角为研究理念,风速预测的研究采用了从全球预报资料GFS的预报输入,到WRF模式的核心处理,直至基于GrADS软件的可视化输出,形成了立体化的风速预测框架基调。
(4)针对风电场风功率进行预测是缓解电力系统调峰压力,提高风电接纳能力的有效手段之一。本文将风速预测法和风功率曲线预测法有机结合,进行风电场风功率预测的研究。首先,采用直接法、比恩法、最大值法和幂函数法分别建立风电场风力发电机组总的功率曲线；其次,把四种方法所建立的风功率曲线与基于WRF模式的风速预测相结合,进行预测时效为12小时和24小时的风功率预测研究；最后,运用平均相对误差法、均方根法、相关系数法和风电功率预报准确率法针对不同方法下得出的预测功率值与山东安成风电场实际风功率输出值进行分析比对,经综合分析和验证,运算简捷的非线性幂函数法不失为提高风功率预测精度的有效方法。
(5)针对大型风电场并网,风电场输出功率的波动必然影响电网输电能力问题,以及在以往输电断面内输电线路载荷能力求解过程中电源节点端电压维持不变(PV节点)的假设,使输电线路输电能力计算偏于乐观等问题,首先,在输电线路热载荷为DTR热定值背景下,建立了基于扩展潮流,考虑大型风电场和同步发电机组发电约束的计算输电断面最大输电能力的潮流模型和方法。扩展潮流打破了PV、PQ和平衡节点的假设,将描述元件动态特性的微分方程加入到潮流计算中,将传统潮流方程与动态元件的状态方程联立求取其稳态解,同时解出系统中各个节点的电压、相角、各种动态元件内部的状态变量,比传统潮流计算得出了更多的信息,更加全面地描述了电力系统的模型；其次,基于扩展潮流模型和方法,分析大型风电场并网位置、风速和风功率变化及同步发电机调速器和励磁系统等动态元件运行限制对电网输电能力的影响；最后,通过算例分析验证了由于扩展潮流模型中考虑了各类电源的发电约束对电网输电能力的影响,计算结果更符合实际,并得出结论,计算最大输电能力的时候必须考虑到未来风速变化产生的影响,特别是要考虑到因为风速过低或过高导致风电机组被切除以后最大输电能力的显著变化。
With the proposition of the energy-saving and the emission-reduction policies, energy problem has become the focus of research. The source has been rapidly developed. Renewable resources energy such as the recyclable and non-Polluting renewable wind energy has attracted the attention of the world. The wind power output is very dependent on the wind speed which is fluctuant, intermittent and stochastic. With the more and more wind power plants on power systems, it will bring challenges to the safety and stabilization of the power system. With the development of the economy and the progressing of the society, the contradiction between power supply and demand is becoming more and more obvious. Such as, in the real operating conditions of power system, how to accurately calculate the transmission capacity of overhead transmission line, how to develop the potential ability of overhead transmission lines, how to improve efficiency of the existing power system to alleviate the lack of transmission capacity. Above at all, these have become the focus study of the academic and engineering field.
At present, the DTR technology has been more mature. DTR technology has achieved real-time tracking of the operating transmission line temperature and thermal rating through variable measuring, communication equipments fixed on transmission lines. DTR technology develops effectively the potential of the transmission capacity. Based on the transmission line dynamic thermal rating and large-scale wind plants in the power system, the study of the transmission capacity of overhead transmission lines puts forward some new problems in the depth and breadth. Such as:because the DTR technology depends on hardware and high cost of operation and maintenance, it is difficult to be spread. Based on the DTR technology and taking into account voltage level and steady-state-stability limitation, how to solve the online valuing loadability under operation. With the change of the wind speeds, how improve the forecasting accuracy of the wind speed and wind power. With wind power fluctuations about the large-scale wind plants on the power grid, how to affect the power transfer capacity and so on.
Consequently, in the paper we make an in-depth study of the transmission capacity of overhead transmission lines based on the background of the dynamic thermal rating and wind plants on the power grid. The main contents and results are as follows:
(1) For the DTR technology depending on the hardware and its expensive operation and maintenance costs, we give a method to realize DTR function in only measuring the carrying current and temperature of transmission line. Firstly, the calculation formula for the IEEE standard transmission line thermal balance equation of the coefficients and its physical significance are described in detail. On the basis of that, the discrete heat balance equation of transmission line are obtained, and the comprehensive effects of wind direction, wind speed, sunshine and environmental temperature are equivalent to the discrete parameters of the heat balance equation of transmission line. Secondly, limited memory generalized least squares estimation method is used to estimate the heat balance equation of discrete parameter online. This method overcomes the problem that the gain matrix of the classical recursive least square method with time gradually tends to zero and losing correction capability, obtained the consistent, unbiased estimator of discrete parameters of heat balance equation. In the process of continuous sampling which sampling time is short, the time-varying discrete parameters of heat balance equation are approximated continuous identified by a recursive process. So, the purpose that heat balance equation of transmission line is modeled online has been reached. Again, the discrete parameters of heat balance equation of transmission line are known, and real-time value on thermal loadability is determined online in transmission line of long-term and short-term. Finally, the example analysis shows that loadability of thermal balance equation of transmission line based on discrete parameters are determined online simplifies the meteorological conditions of measurement system. To determine the real-time value of long-term and short-term maximum permissible carrying current online, it is only need to measure carrying current and temperature of transmission line, which excavates the transmission potential of transmission line effectively.
(2) Based on the DTR technology and taking into account thermal rating, voltage level and steady-state-stability limitation, the model and algorithm of online valuing loadability under operation are established. First, through a simplified transmission line equivalent model of the sending and receiving end system, we make a qualitative analysis of online loadability under an operating system. It shows that the loadability is reflected by sending and receiving end equivalent parameters on transmission lines in different voltage levels. The real-time system equivalent model is very necessary for us to analyze the loadability. Secondly, taking the system as a whole and considering the effect of parallel flow on the transmission line loadability in interconnected system, the dual-end resource equivalent model of the sending and receiving end system is proposed by the system node impedance matrix transform and equivalents simplification. The derivation of the model is simple, and it has a clear physical meaning. Finally, based on state estimation in power system, equivalent parameter on-line tracking is realized. Considering the thermal rating, voltage level and steady-state-stability limitation, the analysis and calculation of online valuing loadability of transmission line are implemented. The feasibility and effectiveness of the equivalent model and the method are confirmed by the analysis of the loadability of Shandong220kV transmission line under operation.
(3) Accurate forecasting of wind speed is helpful to improve the accuracy of wind power forecasting. It can also effectively relieve or avoid the disadvantageous impact of wind power plants on power systems and enhance the competitive ability of the wind power plants against other power plants in electricity markets. First of all, without the need for a large number of historical data, the wind speed is predicted by the new generation mesoscale forecasting model of WRF. The WRF provides high resolution and priority design, locates effectively the wind plants, captures the characteristics of weather system and uses different physical options and advanced data assimilation system to establish various wind speed forecasting models. Secondly, according to various physical schemas, we forecast wind speed respectively aging for12hours and24hours. Finally, compared with the real measured wind speed data obtained from the SCADA system of Shandong Ancheng wind plant, the wind speed forecasting based on WRF is a new effective method to improve the forecasting accuracy by analysis of the error. In this paper, the target is a utility of display and design based on WRF's the wind speed forecasting. The utility study of wind speed forecasting consists of input files based on GFS, the WRF core processing and the visualization output based on GrADS. It forms a new framework of three-dimensional wind speed forecasting.
(4) The wind power forecasting of wind plants is one of the effective means to ease the peak pressure of the power system and to improve the wind power capacity. The paper studies wind power forecasting by organically combining the wind speed forecasting method and wind power curve forecasting method. First of all, the direct method, the Bean method, the maximum value method and the power function method are used to establish the total power curve of wind plants. Secondly, the wind power curves of four kinds of the established method are combined with the wind speed forecasting based on WRF to study wind power forecasting aging for12hours and24hours. Finally, forecasting power obtained with different methods is compared with the wind power actual output values from the Shandong Ancheng wind plant by average relative error, root-mean-square method, correlation coefficient method and wind power forecast accuracy method. Through comprehensive analysis and verification, nonlinear power function method is a simple and effective method of improving the wind power forecasting accuracy.
(5) For large-scale wind plant on the power system, wind power output fluctuations will inevitably affect the power system transmission capacity. Transmission capacity is somewhat optimistic for some of the assumptions, which the terminal voltage (PV nodes) remains unchanged. First, under the thermal loadability of the transmission line based on the DTR, taking into account wind power plants and the generator units'operating restrictions on the power transmission capability, the power flow model and the corresponding algorithm based on expending power flow are established for the calculation of TTC. Extended power flow broke PV, PQ and the balance node hypothesis. After adding the component dynamic characteristics of the differential equation into the flow calculation, the steady-state solution is obtained by simultaneous calculating of the traditional flow equations and dynamic component equations, and we can solute each node voltage, phase angle, internal state variables of the dynamic element at the same time in the system. Comparing with the traditional flow calculation, we get more information and get a more comprehensive description of the model of the power system. Secondly, based on the models and methods of expanded power flow, we study the impact on the power transmission capacity through the analysis of Large-scale wind plants position, changing wind speed and wind power, and dynamic characteristics of operating synchronous generators and excitation system. Finally, the real example analysis results prove that the effects of the total transfer capability are accord with the actual situation. It is indicated that the impact on calculating the TTC of power system containing large changing wind speed, especially which is too low or too high to result that the wind turbine is removed from the power system, should be considered.
目前,动态热定值(dynamic thermal rating, DTR)技术已较为成熟,DTR技术通过输电线路上增加各种测量、通信设备,实现对输电线路运行温度、热定值的实时跟踪和计算,有效挖掘了输电线路的输电潜力,输电线路动态热定值和大型风电场并网对架空输电线路输电能力的研究,无论从深度和广度上都提出若干新的问题,如：DTR技术硬件的依赖性及其运行维护的昂贵费用,使其应用难以普及的问题；DTR热定值基础上,综合考虑电压水平和功角静稳等电力系统运行限制的运行条件下输电线路载荷能力定值问题；风速变化是随机的,风速及风功率的准确预测问题；大型风电场并网,风电场输出功率的波动影响电网输电能力的问题等,为此,本文在动态热定值环境下,及风电场并网背景下,对架空输电线路输电能力进行了深入的研究。研究的主要内容和成果如下：
(1)针对DTR技术对硬件的依赖性及其运行维护的昂贵费用,给出了在仅需测量输电线路载流和导线温度的情况下实现DTR功能的方法。首先,对IEEE标准的输电线路热平衡方程各项系数的计算公式及其相关的物理意义进行了描述,在此基础上,将输电线路热平衡方程离散化,将风向、风速、日照、环境温度等综合影响通过输电线路热平衡方程离散参数来进行等效；其次,利用限定记忆广义最小二乘递推估计方法实现热平衡方程离散参数的在线估计,该方法克服了经典最小二乘递推法的增益矩阵随时间推移渐趋于零而导致的修正能力降低问题,获得了热平衡方程离散参数的一致、无偏估计。在采样时间较短的连续采样过程中,通过递推过程实现了热平衡方程时变离散参数的近似连续辨识,达到了对输电线路热平衡方程在线建模的目的；再次,在输电线路热平衡方程离散参数已知的情况下,方便地实现了输电线路长期和短期热载荷能力的实时在线定值；最后,通过算例分析表明基于输电线路热平衡方程离散参数的输电线路载流量在线定值简化了气象条件量测系统,只需测量输电线路载流量和导线温度即可实现输电线路长期和短期最大允许载流量实时在线定值,从而有效挖掘输电线路的输电潜力。
(2)针对运行条件下的输电线路载荷能力除了受热限制外,还受到电压水平和功角静稳等电力系统运行限制,在DTR热定值基础上,建立了运行条件下交流输电线路载流量在线定值模型和算法。首先,通过简化的输电线路送、受端系统等值模型,对线路运行的系统实时状态对线路载荷能力的影响进行了定性分析,其具体体现在分析不同电压等级下,输电线路送受端系统简化等值参数对线路载流量的影响,从而说明了建立实时状态下系统等值模型在线分析输电线路载荷能力的必要性；其次,通过系统节点阻抗矩阵的变换和等值化简,推导出建立考虑系统的整体性,体现并行流对输电线路载荷能力影响的送受端系统双端电源等值模型。该模型推导过程简单,且物理意义明确；最后,基于状态估计,实现在线跟踪双端电源等值参数,并综合考虑热电流、电压和静态稳定限制,实现运行条件下输电线路载荷能力在线定值计算,通过山东电网220kV输电线路载流量在线定值的计算验证了模型和计算方法的有效性。
(3)精确的风速预测,有助于风电场风功率预测的精度提高,有效地减轻或避免风电并网带来的不利影响。首先,在不需要大量的历史数据资料情况下,采用新一代中尺度非静力WRF预报模式进行风速预测,利用WRF模式提供的高分辨率优先级设计,有效定位风电场地理位置,捕获天气系统的特征,利用不同物理方案的选择和先进的资料同化系统,建立多种风速预测模型方案；其次,针对各种不同的方案分别进行预测时效为12小时和24小时的风速预测,最后,与由山东安成风电场SCADA系统获得的风电场实测风速数据相比较,通过误差分析验证基于WRF模式的风速预测是提高预测精度的新的有效方法。本文基于WRF模式风速预测以全方位的展示与设计视角为研究理念,风速预测的研究采用了从全球预报资料GFS的预报输入,到WRF模式的核心处理,直至基于GrADS软件的可视化输出,形成了立体化的风速预测框架基调。
(4)针对风电场风功率进行预测是缓解电力系统调峰压力,提高风电接纳能力的有效手段之一。本文将风速预测法和风功率曲线预测法有机结合,进行风电场风功率预测的研究。首先,采用直接法、比恩法、最大值法和幂函数法分别建立风电场风力发电机组总的功率曲线；其次,把四种方法所建立的风功率曲线与基于WRF模式的风速预测相结合,进行预测时效为12小时和24小时的风功率预测研究；最后,运用平均相对误差法、均方根法、相关系数法和风电功率预报准确率法针对不同方法下得出的预测功率值与山东安成风电场实际风功率输出值进行分析比对,经综合分析和验证,运算简捷的非线性幂函数法不失为提高风功率预测精度的有效方法。
(5)针对大型风电场并网,风电场输出功率的波动必然影响电网输电能力问题,以及在以往输电断面内输电线路载荷能力求解过程中电源节点端电压维持不变(PV节点)的假设,使输电线路输电能力计算偏于乐观等问题,首先,在输电线路热载荷为DTR热定值背景下,建立了基于扩展潮流,考虑大型风电场和同步发电机组发电约束的计算输电断面最大输电能力的潮流模型和方法。扩展潮流打破了PV、PQ和平衡节点的假设,将描述元件动态特性的微分方程加入到潮流计算中,将传统潮流方程与动态元件的状态方程联立求取其稳态解,同时解出系统中各个节点的电压、相角、各种动态元件内部的状态变量,比传统潮流计算得出了更多的信息,更加全面地描述了电力系统的模型；其次,基于扩展潮流模型和方法,分析大型风电场并网位置、风速和风功率变化及同步发电机调速器和励磁系统等动态元件运行限制对电网输电能力的影响；最后,通过算例分析验证了由于扩展潮流模型中考虑了各类电源的发电约束对电网输电能力的影响,计算结果更符合实际,并得出结论,计算最大输电能力的时候必须考虑到未来风速变化产生的影响,特别是要考虑到因为风速过低或过高导致风电机组被切除以后最大输电能力的显著变化。
With the proposition of the energy-saving and the emission-reduction policies, energy problem has become the focus of research. The source has been rapidly developed. Renewable resources energy such as the recyclable and non-Polluting renewable wind energy has attracted the attention of the world. The wind power output is very dependent on the wind speed which is fluctuant, intermittent and stochastic. With the more and more wind power plants on power systems, it will bring challenges to the safety and stabilization of the power system. With the development of the economy and the progressing of the society, the contradiction between power supply and demand is becoming more and more obvious. Such as, in the real operating conditions of power system, how to accurately calculate the transmission capacity of overhead transmission line, how to develop the potential ability of overhead transmission lines, how to improve efficiency of the existing power system to alleviate the lack of transmission capacity. Above at all, these have become the focus study of the academic and engineering field.
At present, the DTR technology has been more mature. DTR technology has achieved real-time tracking of the operating transmission line temperature and thermal rating through variable measuring, communication equipments fixed on transmission lines. DTR technology develops effectively the potential of the transmission capacity. Based on the transmission line dynamic thermal rating and large-scale wind plants in the power system, the study of the transmission capacity of overhead transmission lines puts forward some new problems in the depth and breadth. Such as:because the DTR technology depends on hardware and high cost of operation and maintenance, it is difficult to be spread. Based on the DTR technology and taking into account voltage level and steady-state-stability limitation, how to solve the online valuing loadability under operation. With the change of the wind speeds, how improve the forecasting accuracy of the wind speed and wind power. With wind power fluctuations about the large-scale wind plants on the power grid, how to affect the power transfer capacity and so on.
Consequently, in the paper we make an in-depth study of the transmission capacity of overhead transmission lines based on the background of the dynamic thermal rating and wind plants on the power grid. The main contents and results are as follows:
(1) For the DTR technology depending on the hardware and its expensive operation and maintenance costs, we give a method to realize DTR function in only measuring the carrying current and temperature of transmission line. Firstly, the calculation formula for the IEEE standard transmission line thermal balance equation of the coefficients and its physical significance are described in detail. On the basis of that, the discrete heat balance equation of transmission line are obtained, and the comprehensive effects of wind direction, wind speed, sunshine and environmental temperature are equivalent to the discrete parameters of the heat balance equation of transmission line. Secondly, limited memory generalized least squares estimation method is used to estimate the heat balance equation of discrete parameter online. This method overcomes the problem that the gain matrix of the classical recursive least square method with time gradually tends to zero and losing correction capability, obtained the consistent, unbiased estimator of discrete parameters of heat balance equation. In the process of continuous sampling which sampling time is short, the time-varying discrete parameters of heat balance equation are approximated continuous identified by a recursive process. So, the purpose that heat balance equation of transmission line is modeled online has been reached. Again, the discrete parameters of heat balance equation of transmission line are known, and real-time value on thermal loadability is determined online in transmission line of long-term and short-term. Finally, the example analysis shows that loadability of thermal balance equation of transmission line based on discrete parameters are determined online simplifies the meteorological conditions of measurement system. To determine the real-time value of long-term and short-term maximum permissible carrying current online, it is only need to measure carrying current and temperature of transmission line, which excavates the transmission potential of transmission line effectively.
(2) Based on the DTR technology and taking into account thermal rating, voltage level and steady-state-stability limitation, the model and algorithm of online valuing loadability under operation are established. First, through a simplified transmission line equivalent model of the sending and receiving end system, we make a qualitative analysis of online loadability under an operating system. It shows that the loadability is reflected by sending and receiving end equivalent parameters on transmission lines in different voltage levels. The real-time system equivalent model is very necessary for us to analyze the loadability. Secondly, taking the system as a whole and considering the effect of parallel flow on the transmission line loadability in interconnected system, the dual-end resource equivalent model of the sending and receiving end system is proposed by the system node impedance matrix transform and equivalents simplification. The derivation of the model is simple, and it has a clear physical meaning. Finally, based on state estimation in power system, equivalent parameter on-line tracking is realized. Considering the thermal rating, voltage level and steady-state-stability limitation, the analysis and calculation of online valuing loadability of transmission line are implemented. The feasibility and effectiveness of the equivalent model and the method are confirmed by the analysis of the loadability of Shandong220kV transmission line under operation.
(3) Accurate forecasting of wind speed is helpful to improve the accuracy of wind power forecasting. It can also effectively relieve or avoid the disadvantageous impact of wind power plants on power systems and enhance the competitive ability of the wind power plants against other power plants in electricity markets. First of all, without the need for a large number of historical data, the wind speed is predicted by the new generation mesoscale forecasting model of WRF. The WRF provides high resolution and priority design, locates effectively the wind plants, captures the characteristics of weather system and uses different physical options and advanced data assimilation system to establish various wind speed forecasting models. Secondly, according to various physical schemas, we forecast wind speed respectively aging for12hours and24hours. Finally, compared with the real measured wind speed data obtained from the SCADA system of Shandong Ancheng wind plant, the wind speed forecasting based on WRF is a new effective method to improve the forecasting accuracy by analysis of the error. In this paper, the target is a utility of display and design based on WRF's the wind speed forecasting. The utility study of wind speed forecasting consists of input files based on GFS, the WRF core processing and the visualization output based on GrADS. It forms a new framework of three-dimensional wind speed forecasting.
(4) The wind power forecasting of wind plants is one of the effective means to ease the peak pressure of the power system and to improve the wind power capacity. The paper studies wind power forecasting by organically combining the wind speed forecasting method and wind power curve forecasting method. First of all, the direct method, the Bean method, the maximum value method and the power function method are used to establish the total power curve of wind plants. Secondly, the wind power curves of four kinds of the established method are combined with the wind speed forecasting based on WRF to study wind power forecasting aging for12hours and24hours. Finally, forecasting power obtained with different methods is compared with the wind power actual output values from the Shandong Ancheng wind plant by average relative error, root-mean-square method, correlation coefficient method and wind power forecast accuracy method. Through comprehensive analysis and verification, nonlinear power function method is a simple and effective method of improving the wind power forecasting accuracy.
(5) For large-scale wind plant on the power system, wind power output fluctuations will inevitably affect the power system transmission capacity. Transmission capacity is somewhat optimistic for some of the assumptions, which the terminal voltage (PV nodes) remains unchanged. First, under the thermal loadability of the transmission line based on the DTR, taking into account wind power plants and the generator units'operating restrictions on the power transmission capability, the power flow model and the corresponding algorithm based on expending power flow are established for the calculation of TTC. Extended power flow broke PV, PQ and the balance node hypothesis. After adding the component dynamic characteristics of the differential equation into the flow calculation, the steady-state solution is obtained by simultaneous calculating of the traditional flow equations and dynamic component equations, and we can solute each node voltage, phase angle, internal state variables of the dynamic element at the same time in the system. Comparing with the traditional flow calculation, we get more information and get a more comprehensive description of the model of the power system. Secondly, based on the models and methods of expanded power flow, we study the impact on the power transmission capacity through the analysis of Large-scale wind plants position, changing wind speed and wind power, and dynamic characteristics of operating synchronous generators and excitation system. Finally, the real example analysis results prove that the effects of the total transfer capability are accord with the actual situation. It is indicated that the impact on calculating the TTC of power system containing large changing wind speed, especially which is too low or too high to result that the wind turbine is removed from the power system, should be considered.
引文
[1]中国可再生能源学会风能专业委员会.2012年中国风电装机容量统计.http://www.cwea.org.cn.
[2]舒印彪,刘泽洪等.2005年国家电网公司特高压输电论证工作综述[J].电网技术,2006,30(5)：1-12.
[3]宋永华,孙静.未来欧洲的电网发展与电网技术[J].电力技术经济,2008,20(5)： 1-5.
[4]Pullins S. Westerman J. San siego smart grid study final report [R]. Science Application International Corporation,2006.
[5]陈树勇,宋书芳等.智能电网技术综述[J].电网技术,2009,33(8)：1-7.
[6]IEEE Std738-2006, IEEE Standard for Calculating the Current -Temperature of Bare Overhead Conductors. IEEE Power Engineering Society,2007.
[7]IEC Technical Report 1597, Overhead Electrical Conductors-Calculation Methods for Stranded Bare Conductors, IEC 1995-05.
[8]CIGRE Working Group 22.12 ELT_144, The thermal behavior of overhand conductors, Electra 107-125, October 1992.
[9]徐青松,季洪献,侯炜,等.监测导线温度实现输电线路增容新技术[J].电网技术,2006,30(增刊)：171-176.
[10]张启平,钱之银.输电线路实时动态增容的可行性研究[J].电网技术,2005,29(19)：18-21.
[11]张辉,韩学山,王艳玲.架空输电线路运行载流量分析[J].电网技术,2008,32(14)：31-35.
[12]Davis M W. A new thermal rating approach:the real time thermal rating system for strategic overhead conductor transmission lines, part I[J]. IEEE Transactions on Power Apparatus and Systems,1977,96(3):803-809.
[13]Davis M W. A new thermal rating approach:the real time thermal rating system for strategic overhead conductor transmission lines, par Ⅱ[J]. IEEE Transactions on Power Apparatus and Systems,1977,96(3):810-825.
[14]Davis M W. A new thermal rating approach:the real time thermal rating system for strategic overhead conductor transmission lines, part Ⅲ[J]. IEEE Transactions on Power Apparatus and Systems,1977,96(3):444-455.
[15]陆鑫淼.动态提高输电线路输送容量计算模型以及通信平台软件开发[D].上海交通大学,2009.
[16]徐青松,陈宁,侯炜,等.输电线路动态热定额技术的应用[J].电力建设,2007,28(7)：28-30,33.
[17]黄新波,孙钦东,张冠军,程荣贵.输电线路实时增容技术的理论计算与应用研究[J].高电压技术,2008,,Vo1.34(6)：1138-1144.
[18](1999) Consultant report for California Energy Comission. Dynamic circuit thermal line rating. SanDiego.CA. Available:http://www.energy.ca.gov/reports/2002-01-10_600-00-036.PDF.
[19](2003) Consultant report for California Energy Comission. Development of a real-time monitoring/dynamic rating system for overhead lines. EDM International Inc. Available:http://www.energy.ca.gov/reports/2004-04-02_500-04-003.PDF
[20]M. W. Davis. A new thermal rating approach:the real time thermal rating system for strategic overhead conductor transmission lines-Part Ⅳ, Daliy comparisons of real-time and conventional thermal ratings and establishment of typical annual weather models [J]. IEEE Transactions on Power Apparatus and Systems,1980,99(6):2184-2192.
[21]G. J. Ramon. Dynamic thermal line rating summary and status of the state-of-the art technology [J]. IEEE Transactions on Power Delivery,1987, 3(3):851-858.
[22]Patrick M. Callahan, Dale A. Douglas. An experimental evaluation of a thermal line uprating by conductor termperature and weather monitoring[J]. IEEE Transactions on Power Delivery,1988,3(4):1960-1967.
[23]任丽佳,盛戈皞,李力学,等.动态确定输电线路输送容量[J].电力系统自动化,2006,30(17)：45-49.
[24]任丽佳,江秀臣,曾奕.提高输电线路输送容量DLR系统的相关理论研 究》[J].高压电气,2008,44(3)：250-253.
[25]陈芳,韩学山等.基于相量测量单元的输电线路温度跟踪估计[J].电力系统自动化,2009,33(19)：25-29.
[26]陈芳,韩学山,康凯等.基于SCADA信息追踪输电线路动态热定值[J].电力系统自动化,2010,34(5)：81-85.
[27]陈芳.电网状态估计及其扩展的理论研究[D],山东大学,2010.
[28]韩芳,徐青松,侯炜,等.架空导线动态载流量计算方法的应用[J].电力建设,2008,29(1)：39-43.
[29]Glenn A. Davidson, Thomas E. Donoho, Pierre R. H. Landrieu, et.al. Short-time thermal ratings for bare overhead conductors[J]. IEEE Transactions on Apparatus and Systems,1969,88(3):194-199.
[30]T. Y. Wong, J. A. Findlay, A. N. McMurtrie. An on-line method for transmission ampacity evaluation[J]. IEEE Transactions on Apparatus and Systems,1982,101(2):309-315.
[31]张辉.运行条件下输电线路热载荷能力研究[D].山东大学,2008.
[32]王孟夏.电网运行的电热协调理论研究[D].山东大学,2011.
[33]方崇智,萧德云编著.《过程辨识》[M].清华大学出版社,2006.
[34]刘党辉,蔡远文等编著.《系统辨识方法及应用》[M].国防工业出版社,2010
[35]常康,韩学山,王孟夏,等.电网关键元件及其单调性研究Ⅰ：概念与基础[J].电力系统保护与控制,2009,37(6)：1-5.
[36]常康,韩学山,王孟夏,等.电网关键元件及其单调性研究Ⅱ：机理与证明[J].电力系统保护与控制,2009,37(7)：1-6.
[37]常康,韩学山,韩力,等.电网关键元件及其单调性研究Ⅲ：应用[J].电力系统保护与控制,2009,37(8)：1-4.
[38]Dunlop R D, Gutman R, Marchenko P P. Analytical development of loadability characteristics for EHV and UHV transmission line[J]. IEEE Trans. on Power Apparatus and System,1979, PAS98(2):606-613.
[39]H. P. St. Clair, Practical Concepts in Capability and Performance of Transmission Lines[J], AIEE Transactions on Power Apparatus and Systems, Vol.72 Part Ⅲ, pp 1152-1157, December 1953.
[40]Gutman R. Application of loadability to operating studies[J]. IEEE Trans. on Power Systems,1988,3(4):1426-1433.
[41]LaForest J J. Transmission line reference book 345kV and above (second edition) [M]. Palo Alto, California:Electric Power Research Institute,1982.
[42]Tiwari S N, Binsaroor A S. An investigation into loadability characteristics of EHV high phase order[J]. IEEE Trans..on PowerSystems,1995,10(3): 1264-1270.
[43]徐政.超、特高压交流输电系统的输送能力分析[J].电网技术,1995,19(8)： 7-12.
[44]柴旭峥,梁曦东,曾嵘.交流输电线路输送能力曲线计算方法的改进[J].电网技术,2005,29(24)：20-24.
[45]孔令元,韩学山,王孟夏等.输电线路载荷能力在线定值研究[C].第25届中国高等学校电力系统及其自动化专业年会,2009,中国湖南长沙.
[46]孔令元,输电线路载荷能力在线定值研究[D].山东大学,2010.
[47]王艳玲,韩学山,等.运行条件下输电线路了载荷能力定值[J].电力系统及其自动化学报,2012,24(5)：42-48.
[48]李听伟. 电网输电元件在线定值系统研究与实践[D].山东大学.2012.
[49]雷亚洲,王伟胜,戴慧珠,等.风电对电力系统运行的价值分析[J].电网技术,2002,26(5)：10-14.
[50]吴国旸,肖洋,翁莎莎.风电场短期风速预测探讨[J].吉林电力,2005,(6)：21-24.
[51]刘永前,韩爽,胡永生.风电场出力短期预报研究综述[J].现代电力,2007,24(90)：6-11.
[52]谷兴凯,范高锋,王晓蓉,等.风电功率预测技术综述[J].电网技术,2007,31(增2)：335-338.
[53]范高锋,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.
[54]韩爽.风电场功率短期预测方法研究[D].华北电力大学,2008.
[55]杨志凌.风电场功率短期顶测方法优化的研究[D].华北电力大学,2011.
[56]丁明,张立军,吴义纯.基于时间序列分析的风电场风速预测模型[J].电力自动化设备,2005,25(8)：32-34.
[57]杨秀媛,肖洋等.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.
[58]潘迪夫,刘辉,李燕飞.基于时间序列分析和卡尔曼滤波算法的风电场风速预测优化模型[J].电网技术,2008,32(7)：82-86.
[59]杜颖,卢继平,李青,邓颖玲.基于最小二乘支持向量机的风电场短期风速预测[J].电网技术,2008,32(15)：62-66.
[60]张国强,张伯明.基于组合预测的风电场风速及风电机功率预测[J].电力系统自动化,2009,33(18)：92-95.
[61]冯双磊,王伟胜,刘纯.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.
[62]中国电科院开发的江苏电网风电功率预测系统通过验收.电气技术,2010,第2期：5.
[63]Mohamed A Mohandes, Shafiqur Rehman, Talal O Halawani. Aneural networks approach for wind speed prediction [J]. RenewableEnergy,1998, 13(3):345-354.
[64]T S Nielsen, H Madsen, WPPT-a tool for wind power prediction[C]. EWEA Special Topic Conference, Kassel,2000.
[65]Nicholas Cutler, Merlinde Kay, Kieran Jacka and Torben Skov Nielsen. Detecting, categorizing and forecasting large ramps in wind farm power output using meteorological observations and WPPT.[J] Wind Energy, Vol.10, No.5, pp.453-470, September/October 2007.
[66]Henrik Aalborg Nielsen, Henrik Madsen and Torben Skov Nielen. Using quanti le regression to extend an existing wind power forecasting system With probabilistic forecasts[J]. Wind Energy, Vol.9, No.1-2, pp.95-108, January/April 2006.
[67]U M Focken, H P W Lange, Previento-a wind power prediction system with an innovative upscaling algorithm[C]. European Wind Energy Conference, Copenhagen, Denmark,2001.
[68]<风电功率预测系统及其方法、电网系统>.专利权人：北京方鸿溪科技有限公司.专利号：200810180041.
[69]湖北省气象局牵头研制的“风电功率预测预报系统”.2011
[70]孙川永,陶树旺,罗勇等.高分辨率中尺度数值模式在风电场风速预报中的应用[J].太阳能学报2009,30(8)：1097-1099.
[71]孙川永.风电场风电功率短期预报技术研究[D].兰州大学,2009.
[72]中国气象局风能太阳能资源评估中心.中国风能资源评估(2009)[M].气象出版社,2010.1-37.
[73]龚强,袁国恩,等.MM5模式在风能资源普查中的应用试验[J].资源科学,28(1)：145-150,2006.
[74]Michael Duta. The WRF Preprocessing System Description of General Functions. Summer 2008 WRF Users'tutorial.2008.
[75]ARW Versions 3 Modeling System User's Guide. Mesoscale and Microscale Meteorology Division.National Center for Automospheric Research, July 2012.
[76]William C. Skamarock, Joseph B. Klemp. A Description of the Advanced Research WRF Version 3. Mesoscale and Microscale Meteorology Division.National Center for Automospheric Research, June 2008.
[77]汤浩,贾丽红.美国ARW模式系统简介[J].新疆气象,2006.
[78]胡向军,陶健红,郑飞,王娜,张铁军,刘世祥,尚大成.WRF模式物理过程参数化方案简介[J].甘肃科技.2008,24(20)：73-75.
[79]张鸿雁,丁裕国,等.湖北省风能资源分布的数值模拟[J].气象与环境科学,2008,31(2),35-38.
[80]邓国卫,高晓清,等.酒泉地区风能资源开发优势度分析[J].高原气象,2010,29(6)：1634-1640.
[81]潘丽丽.基于WRF模式的江苏沿海风能资源评估研究[D].南京信息工程大学,2009.
[82]Storm Brandon, Dudhia Jimy, Basu,Sukanta, et al. Evaluation ofthe Weather Research and Forecasting Model on Forecasting Low-level jets: Implications for Wind Energy[J]. Wind Energy,2009,12(1):81-90.
[83]陈玲,赖旭,等.WRF模式在风电场风速预测中的应用[J].武汉大学学报,2012,45(1)：103-106.
[84]张华,孙科,等.应用WRF模型模拟分析风力发电场风速[J].天津大学学报,45(12)：1160-1120
[85]程兴宏.基于WRF模式和自适应偏最小二乘回归法的风能预报试验研究[J].高原气象,2012.31(5)：1461-1469.
[86]江滢,李忠,等.风电场风速和风电功率预报准确率评判方法[J].科技导报,2012,30(36)：66-71.
[87]刘兴杰.风电输出功率预测方法与系统[D].华北电力大学,2010.
[88]孙元章,吴俊,李国杰.风力发电对电力系统的影响[J].电网技术,2007,31(20)： 55-62.
[89]周明,冉瑞江等.风电并网系统可用输电能力评估[J].中国电机工程学报,2010,30(22)：14-19.
[90]吴俊玲,周双喜,等.并网风力发电场的最大注入功率分析[J].电网技术,2004,28(20)：28-32.
[91]FEIJOO A E, CIDRAS J. Modeling of Wind Farms in the Load Flow Analysis[J]. IEEE Trans on Power Systems,2000,15(1):110-115.
[92]FUERTE-ESQUIVEL C R, TOVAR-HERNANDEZ J H, GUTIERREZ-ALCARAZ G, et al. Discussion of "Modeling of Wind Farms in the Load Flow Analysis" [J]. IEEE Trans on Power Systems,2001,16(4):951-959.
[93]吴义纯,丁明,张力军.含风电场的电力系统潮流计算[J]..中国电机工程学报,2005,25(4)：36-39.
[94]王成山,孙伟,王兴刚.含大型风电场的电力系统最大输电能力计算[J].电力系统自动化,2007,31(2)：17-21.
[95]邱家驹,韩祯祥,江晓东.潮流计算中的PIf节点[J].中国电机工程学报,1995,15(5)：323-327
[96]朱凌志,周双喜.电压稳定分析的潮流算法研究[J].电力系统自动化,2000,5(11)：1-5.
[97]王庆红,周双喜,胡国根.基于扩展潮流模型的电力系统电压稳定分析[J].电网技术,2002,26(10)：25-29.
[98]Xu W, Mansour Y. Voltage stability analysis using generic dynamic load models[J]. IEEE Trans. on Power Systems,1994,9(1).
[99]马平,蔡兴国.基于扩展潮流模型的电力系统电压稳定分析[J].中国电机工程学报,2007,27(28)：24-28.
[100]王艳玲,韩学山,周晓峰.基于扩展潮流的输电断面最大传输能力[J].电力系统保护与控制,2011,39(13)：20-24
[101]王漪,柳焯.基于戴维南等值的系统参数跟踪估计[J].电网技术,2000,24(11)：28-30.
[102]王芝茗,王漪,徐敬有,等.关键负荷节点集合电网侧戴维南参数预估[J].中国电机工程学报,2002,22(2)：16-20.
[103]DL755-2001.电力系统安全稳定导则[S].
[104]王俊,蔡兴国.基于差分进化算法的动态可用输电能计算研究[J].电力系统保护与控制.2010,38(4)：39-44.
[105]韩学山,李晓波.考虑元件长期载荷容许条件的最大可用输电能力的实用计算方法[J].电网技术,2004,28(24)：10-15.
[106]Ou Yan, Singh C. A ssessment of A vailable Transfer Capability and M argins [J]. IEEE T ransact ions on Power Systems,2002,17 (2):463-468.
[107]李芳.线性规划法最优潮流的实用性研究[D].中国电力科学研究院,2003.
[108]李国庆,李雪峰,沈杰,等.牛顿法和内点罚函数法相结合的概率可用功率交换能力计算[J].中国电机工程学报,2003,23(8)：17-22.
[109]王成山,李国庆,余贻鑫.电力系统区域间功率交换能力的研究(一)连续方法的基本理论及应用[J].电力系统白动化,1999,23(3)：23-26.
[110]王成山,李国庆,余贻鑫.电力系统区域间功率交换能力的研究(二)最大交换功率的模型与算法[J].电力系统自动化,1999,23(4)：5-9.
[111]赵斌.并网异步风力发电机组的暂态稳定性研究[D].重庆大学.2008.
[112]王艳玲.基于扩展潮流的电力系统输电能力分析[D].山东大学,2012
[2]舒印彪,刘泽洪等.2005年国家电网公司特高压输电论证工作综述[J].电网技术,2006,30(5)：1-12.
[3]宋永华,孙静.未来欧洲的电网发展与电网技术[J].电力技术经济,2008,20(5)： 1-5.
[4]Pullins S. Westerman J. San siego smart grid study final report [R]. Science Application International Corporation,2006.
[5]陈树勇,宋书芳等.智能电网技术综述[J].电网技术,2009,33(8)：1-7.
[6]IEEE Std738-2006, IEEE Standard for Calculating the Current -Temperature of Bare Overhead Conductors. IEEE Power Engineering Society,2007.
[7]IEC Technical Report 1597, Overhead Electrical Conductors-Calculation Methods for Stranded Bare Conductors, IEC 1995-05.
[8]CIGRE Working Group 22.12 ELT_144, The thermal behavior of overhand conductors, Electra 107-125, October 1992.
[9]徐青松,季洪献,侯炜,等.监测导线温度实现输电线路增容新技术[J].电网技术,2006,30(增刊)：171-176.
[10]张启平,钱之银.输电线路实时动态增容的可行性研究[J].电网技术,2005,29(19)：18-21.
[11]张辉,韩学山,王艳玲.架空输电线路运行载流量分析[J].电网技术,2008,32(14)：31-35.
[12]Davis M W. A new thermal rating approach:the real time thermal rating system for strategic overhead conductor transmission lines, part I[J]. IEEE Transactions on Power Apparatus and Systems,1977,96(3):803-809.
[13]Davis M W. A new thermal rating approach:the real time thermal rating system for strategic overhead conductor transmission lines, par Ⅱ[J]. IEEE Transactions on Power Apparatus and Systems,1977,96(3):810-825.
[14]Davis M W. A new thermal rating approach:the real time thermal rating system for strategic overhead conductor transmission lines, part Ⅲ[J]. IEEE Transactions on Power Apparatus and Systems,1977,96(3):444-455.
[15]陆鑫淼.动态提高输电线路输送容量计算模型以及通信平台软件开发[D].上海交通大学,2009.
[16]徐青松,陈宁,侯炜,等.输电线路动态热定额技术的应用[J].电力建设,2007,28(7)：28-30,33.
[17]黄新波,孙钦东,张冠军,程荣贵.输电线路实时增容技术的理论计算与应用研究[J].高电压技术,2008,,Vo1.34(6)：1138-1144.
[18](1999) Consultant report for California Energy Comission. Dynamic circuit thermal line rating. SanDiego.CA. Available:http://www.energy.ca.gov/reports/2002-01-10_600-00-036.PDF.
[19](2003) Consultant report for California Energy Comission. Development of a real-time monitoring/dynamic rating system for overhead lines. EDM International Inc. Available:http://www.energy.ca.gov/reports/2004-04-02_500-04-003.PDF
[20]M. W. Davis. A new thermal rating approach:the real time thermal rating system for strategic overhead conductor transmission lines-Part Ⅳ, Daliy comparisons of real-time and conventional thermal ratings and establishment of typical annual weather models [J]. IEEE Transactions on Power Apparatus and Systems,1980,99(6):2184-2192.
[21]G. J. Ramon. Dynamic thermal line rating summary and status of the state-of-the art technology [J]. IEEE Transactions on Power Delivery,1987, 3(3):851-858.
[22]Patrick M. Callahan, Dale A. Douglas. An experimental evaluation of a thermal line uprating by conductor termperature and weather monitoring[J]. IEEE Transactions on Power Delivery,1988,3(4):1960-1967.
[23]任丽佳,盛戈皞,李力学,等.动态确定输电线路输送容量[J].电力系统自动化,2006,30(17)：45-49.
[24]任丽佳,江秀臣,曾奕.提高输电线路输送容量DLR系统的相关理论研 究》[J].高压电气,2008,44(3)：250-253.
[25]陈芳,韩学山等.基于相量测量单元的输电线路温度跟踪估计[J].电力系统自动化,2009,33(19)：25-29.
[26]陈芳,韩学山,康凯等.基于SCADA信息追踪输电线路动态热定值[J].电力系统自动化,2010,34(5)：81-85.
[27]陈芳.电网状态估计及其扩展的理论研究[D],山东大学,2010.
[28]韩芳,徐青松,侯炜,等.架空导线动态载流量计算方法的应用[J].电力建设,2008,29(1)：39-43.
[29]Glenn A. Davidson, Thomas E. Donoho, Pierre R. H. Landrieu, et.al. Short-time thermal ratings for bare overhead conductors[J]. IEEE Transactions on Apparatus and Systems,1969,88(3):194-199.
[30]T. Y. Wong, J. A. Findlay, A. N. McMurtrie. An on-line method for transmission ampacity evaluation[J]. IEEE Transactions on Apparatus and Systems,1982,101(2):309-315.
[31]张辉.运行条件下输电线路热载荷能力研究[D].山东大学,2008.
[32]王孟夏.电网运行的电热协调理论研究[D].山东大学,2011.
[33]方崇智,萧德云编著.《过程辨识》[M].清华大学出版社,2006.
[34]刘党辉,蔡远文等编著.《系统辨识方法及应用》[M].国防工业出版社,2010
[35]常康,韩学山,王孟夏,等.电网关键元件及其单调性研究Ⅰ：概念与基础[J].电力系统保护与控制,2009,37(6)：1-5.
[36]常康,韩学山,王孟夏,等.电网关键元件及其单调性研究Ⅱ：机理与证明[J].电力系统保护与控制,2009,37(7)：1-6.
[37]常康,韩学山,韩力,等.电网关键元件及其单调性研究Ⅲ：应用[J].电力系统保护与控制,2009,37(8)：1-4.
[38]Dunlop R D, Gutman R, Marchenko P P. Analytical development of loadability characteristics for EHV and UHV transmission line[J]. IEEE Trans. on Power Apparatus and System,1979, PAS98(2):606-613.
[39]H. P. St. Clair, Practical Concepts in Capability and Performance of Transmission Lines[J], AIEE Transactions on Power Apparatus and Systems, Vol.72 Part Ⅲ, pp 1152-1157, December 1953.
[40]Gutman R. Application of loadability to operating studies[J]. IEEE Trans. on Power Systems,1988,3(4):1426-1433.
[41]LaForest J J. Transmission line reference book 345kV and above (second edition) [M]. Palo Alto, California:Electric Power Research Institute,1982.
[42]Tiwari S N, Binsaroor A S. An investigation into loadability characteristics of EHV high phase order[J]. IEEE Trans..on PowerSystems,1995,10(3): 1264-1270.
[43]徐政.超、特高压交流输电系统的输送能力分析[J].电网技术,1995,19(8)： 7-12.
[44]柴旭峥,梁曦东,曾嵘.交流输电线路输送能力曲线计算方法的改进[J].电网技术,2005,29(24)：20-24.
[45]孔令元,韩学山,王孟夏等.输电线路载荷能力在线定值研究[C].第25届中国高等学校电力系统及其自动化专业年会,2009,中国湖南长沙.
[46]孔令元,输电线路载荷能力在线定值研究[D].山东大学,2010.
[47]王艳玲,韩学山,等.运行条件下输电线路了载荷能力定值[J].电力系统及其自动化学报,2012,24(5)：42-48.
[48]李听伟. 电网输电元件在线定值系统研究与实践[D].山东大学.2012.
[49]雷亚洲,王伟胜,戴慧珠,等.风电对电力系统运行的价值分析[J].电网技术,2002,26(5)：10-14.
[50]吴国旸,肖洋,翁莎莎.风电场短期风速预测探讨[J].吉林电力,2005,(6)：21-24.
[51]刘永前,韩爽,胡永生.风电场出力短期预报研究综述[J].现代电力,2007,24(90)：6-11.
[52]谷兴凯,范高锋,王晓蓉,等.风电功率预测技术综述[J].电网技术,2007,31(增2)：335-338.
[53]范高锋,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.
[54]韩爽.风电场功率短期预测方法研究[D].华北电力大学,2008.
[55]杨志凌.风电场功率短期顶测方法优化的研究[D].华北电力大学,2011.
[56]丁明,张立军,吴义纯.基于时间序列分析的风电场风速预测模型[J].电力自动化设备,2005,25(8)：32-34.
[57]杨秀媛,肖洋等.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.
[58]潘迪夫,刘辉,李燕飞.基于时间序列分析和卡尔曼滤波算法的风电场风速预测优化模型[J].电网技术,2008,32(7)：82-86.
[59]杜颖,卢继平,李青,邓颖玲.基于最小二乘支持向量机的风电场短期风速预测[J].电网技术,2008,32(15)：62-66.
[60]张国强,张伯明.基于组合预测的风电场风速及风电机功率预测[J].电力系统自动化,2009,33(18)：92-95.
[61]冯双磊,王伟胜,刘纯.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.
[62]中国电科院开发的江苏电网风电功率预测系统通过验收.电气技术,2010,第2期：5.
[63]Mohamed A Mohandes, Shafiqur Rehman, Talal O Halawani. Aneural networks approach for wind speed prediction [J]. RenewableEnergy,1998, 13(3):345-354.
[64]T S Nielsen, H Madsen, WPPT-a tool for wind power prediction[C]. EWEA Special Topic Conference, Kassel,2000.
[65]Nicholas Cutler, Merlinde Kay, Kieran Jacka and Torben Skov Nielsen. Detecting, categorizing and forecasting large ramps in wind farm power output using meteorological observations and WPPT.[J] Wind Energy, Vol.10, No.5, pp.453-470, September/October 2007.
[66]Henrik Aalborg Nielsen, Henrik Madsen and Torben Skov Nielen. Using quanti le regression to extend an existing wind power forecasting system With probabilistic forecasts[J]. Wind Energy, Vol.9, No.1-2, pp.95-108, January/April 2006.
[67]U M Focken, H P W Lange, Previento-a wind power prediction system with an innovative upscaling algorithm[C]. European Wind Energy Conference, Copenhagen, Denmark,2001.
[68]<风电功率预测系统及其方法、电网系统>.专利权人：北京方鸿溪科技有限公司.专利号：200810180041.
[69]湖北省气象局牵头研制的“风电功率预测预报系统”.2011
[70]孙川永,陶树旺,罗勇等.高分辨率中尺度数值模式在风电场风速预报中的应用[J].太阳能学报2009,30(8)：1097-1099.
[71]孙川永.风电场风电功率短期预报技术研究[D].兰州大学,2009.
[72]中国气象局风能太阳能资源评估中心.中国风能资源评估(2009)[M].气象出版社,2010.1-37.
[73]龚强,袁国恩,等.MM5模式在风能资源普查中的应用试验[J].资源科学,28(1)：145-150,2006.
[74]Michael Duta. The WRF Preprocessing System Description of General Functions. Summer 2008 WRF Users'tutorial.2008.
[75]ARW Versions 3 Modeling System User's Guide. Mesoscale and Microscale Meteorology Division.National Center for Automospheric Research, July 2012.
[76]William C. Skamarock, Joseph B. Klemp. A Description of the Advanced Research WRF Version 3. Mesoscale and Microscale Meteorology Division.National Center for Automospheric Research, June 2008.
[77]汤浩,贾丽红.美国ARW模式系统简介[J].新疆气象,2006.
[78]胡向军,陶健红,郑飞,王娜,张铁军,刘世祥,尚大成.WRF模式物理过程参数化方案简介[J].甘肃科技.2008,24(20)：73-75.
[79]张鸿雁,丁裕国,等.湖北省风能资源分布的数值模拟[J].气象与环境科学,2008,31(2),35-38.
[80]邓国卫,高晓清,等.酒泉地区风能资源开发优势度分析[J].高原气象,2010,29(6)：1634-1640.
[81]潘丽丽.基于WRF模式的江苏沿海风能资源评估研究[D].南京信息工程大学,2009.
[82]Storm Brandon, Dudhia Jimy, Basu,Sukanta, et al. Evaluation ofthe Weather Research and Forecasting Model on Forecasting Low-level jets: Implications for Wind Energy[J]. Wind Energy,2009,12(1):81-90.
[83]陈玲,赖旭,等.WRF模式在风电场风速预测中的应用[J].武汉大学学报,2012,45(1)：103-106.
[84]张华,孙科,等.应用WRF模型模拟分析风力发电场风速[J].天津大学学报,45(12)：1160-1120
[85]程兴宏.基于WRF模式和自适应偏最小二乘回归法的风能预报试验研究[J].高原气象,2012.31(5)：1461-1469.
[86]江滢,李忠,等.风电场风速和风电功率预报准确率评判方法[J].科技导报,2012,30(36)：66-71.
[87]刘兴杰.风电输出功率预测方法与系统[D].华北电力大学,2010.
[88]孙元章,吴俊,李国杰.风力发电对电力系统的影响[J].电网技术,2007,31(20)： 55-62.
[89]周明,冉瑞江等.风电并网系统可用输电能力评估[J].中国电机工程学报,2010,30(22)：14-19.
[90]吴俊玲,周双喜,等.并网风力发电场的最大注入功率分析[J].电网技术,2004,28(20)：28-32.
[91]FEIJOO A E, CIDRAS J. Modeling of Wind Farms in the Load Flow Analysis[J]. IEEE Trans on Power Systems,2000,15(1):110-115.
[92]FUERTE-ESQUIVEL C R, TOVAR-HERNANDEZ J H, GUTIERREZ-ALCARAZ G, et al. Discussion of "Modeling of Wind Farms in the Load Flow Analysis" [J]. IEEE Trans on Power Systems,2001,16(4):951-959.
[93]吴义纯,丁明,张力军.含风电场的电力系统潮流计算[J]..中国电机工程学报,2005,25(4)：36-39.
[94]王成山,孙伟,王兴刚.含大型风电场的电力系统最大输电能力计算[J].电力系统自动化,2007,31(2)：17-21.
[95]邱家驹,韩祯祥,江晓东.潮流计算中的PIf节点[J].中国电机工程学报,1995,15(5)：323-327
[96]朱凌志,周双喜.电压稳定分析的潮流算法研究[J].电力系统自动化,2000,5(11)：1-5.
[97]王庆红,周双喜,胡国根.基于扩展潮流模型的电力系统电压稳定分析[J].电网技术,2002,26(10)：25-29.
[98]Xu W, Mansour Y. Voltage stability analysis using generic dynamic load models[J]. IEEE Trans. on Power Systems,1994,9(1).
[99]马平,蔡兴国.基于扩展潮流模型的电力系统电压稳定分析[J].中国电机工程学报,2007,27(28)：24-28.
[100]王艳玲,韩学山,周晓峰.基于扩展潮流的输电断面最大传输能力[J].电力系统保护与控制,2011,39(13)：20-24
[101]王漪,柳焯.基于戴维南等值的系统参数跟踪估计[J].电网技术,2000,24(11)：28-30.
[102]王芝茗,王漪,徐敬有,等.关键负荷节点集合电网侧戴维南参数预估[J].中国电机工程学报,2002,22(2)：16-20.
[103]DL755-2001.电力系统安全稳定导则[S].
[104]王俊,蔡兴国.基于差分进化算法的动态可用输电能计算研究[J].电力系统保护与控制.2010,38(4)：39-44.
[105]韩学山,李晓波.考虑元件长期载荷容许条件的最大可用输电能力的实用计算方法[J].电网技术,2004,28(24)：10-15.
[106]Ou Yan, Singh C. A ssessment of A vailable Transfer Capability and M argins [J]. IEEE T ransact ions on Power Systems,2002,17 (2):463-468.
[107]李芳.线性规划法最优潮流的实用性研究[D].中国电力科学研究院,2003.
[108]李国庆,李雪峰,沈杰,等.牛顿法和内点罚函数法相结合的概率可用功率交换能力计算[J].中国电机工程学报,2003,23(8)：17-22.
[109]王成山,李国庆,余贻鑫.电力系统区域间功率交换能力的研究(一)连续方法的基本理论及应用[J].电力系统白动化,1999,23(3)：23-26.
[110]王成山,李国庆,余贻鑫.电力系统区域间功率交换能力的研究(二)最大交换功率的模型与算法[J].电力系统自动化,1999,23(4)：5-9.
[111]赵斌.并网异步风力发电机组的暂态稳定性研究[D].重庆大学.2008.
[112]王艳玲.基于扩展潮流的电力系统输电能力分析[D].山东大学,2012
© 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 |