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Itcanbearguedthatintheabsenceofpreciseinformationaboutapowersystemload,oneofthemostre...
It can be argued that in the absence of precise information about a power system load, one of the most reliable ways to obtain an accurate model of the load is to apply an identification technique. That is, if field measurements of load quantities (e.g., the voltage and current/power) adequately describing its behavior are available, then a dynamic and/or static equivalent of the load can be obtained by analyzing functional relationships between these quantities.
The current paper is concerned with theoretical and numerical aspects of identification of an aggregate model of power system loads. Identification of both linear and nonlinear models of a power system load is treated. Two identification techniques are presented that belong to the so-called family of output error models. First, the estimation of the load parameters using a linear model is presented, which is followed by the presentation of a nonlinear identification technique. The statistical properties of the proposed identification methods are studied both numerically and analytically. Thus, artificially created data are analyzed numerically and the variance of the obtained estimates is compared with the corresponding Cramér–Rao lower bound. Then, in order to benchmark the identification techniques and validate the analytical load models, field measurements taken at a paper mill were used. The results obtained indicate that the load models describe the actual behavior of the load with high accuracy. Moreover, it is shown that the load model parameters can be accurately identified using the proposed techniques.
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The current paper is concerned with theoretical and numerical aspects of identification of an aggregate model of power system loads. Identification of both linear and nonlinear models of a power system load is treated. Two identification techniques are presented that belong to the so-called family of output error models. First, the estimation of the load parameters using a linear model is presented, which is followed by the presentation of a nonlinear identification technique. The statistical properties of the proposed identification methods are studied both numerically and analytically. Thus, artificially created data are analyzed numerically and the variance of the obtained estimates is compared with the corresponding Cramér–Rao lower bound. Then, in order to benchmark the identification techniques and validate the analytical load models, field measurements taken at a paper mill were used. The results obtained indicate that the load models describe the actual behavior of the load with high accuracy. Moreover, it is shown that the load model parameters can be accurately identified using the proposed techniques.
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可以被争论在没有关于动力系统装载的精确信息时,其中一个最可靠的方方法是得到装载的一个准确模型是申请证明技术。 即,如果装载数量的野外测量(即,电压和潮流或者力量)充分地描述它的行为的是可利用,然后装载的一个动态并且/或者静止等值能通过分析这些数量之间的功能关系获得。 The当前纸与动力系统装载聚集模型的证明的理论和数字方面有关。 动力系统装载的线性和非线性模型的证明被对待。 的二个证明技术属于输出错误模型所谓的家庭提出。 首先,提出装载参量的估计使用一个线性模型的,由一个非线性证明技术的介绍跟随。 提出的证明方法的统计物产数字上和分析被学习。 因此,数字上分析人工地被创造的数据,并且得到的估计的变化与对应的Cramér-Rao最低界面比较。 然后,为了基准点证明技术和确认分析装载模块,被采取在一个造纸厂使用了野外测量。 得到的结果表明装载模块描述装载的实际行为与高精确度的。 而且,显示使用提出的技术,装载模块参量可以准确地被辨认。
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可以说,在没有确切的资料约1电力系统负荷,其中一个最可靠的方式以获取准确的模型的负荷是申请一个识别技术。也就是说,如果实地测量负载量(例如,电压和电流/功率)充分说明其行为的情况下,然后一个动态和/或静态相当于负荷可以得到分析功能之间的关系,这些数量。
目前的文件是有关理论和数值方面的鉴定累计模型的电力系统负荷。鉴定双方的线性和非线性模型的一个电力系统负荷是治疗。 2鉴定技术的介绍,属于那些所谓家庭的输出误差模型。首先,估计负荷参数采用线性模型,这是其次介绍了一种非线性识别技术。统计特性所建议的识别方法,研究了两个数值和分析。因此,人为制造的数据分析和数值的差异,所得到的估计是与相应的cramér -饶下界。然后,在以基准识别技术和验证分析负荷模型,实地测量所采取的在造纸厂使用。所取得的成果表明,该负荷模型描述的实际行为的负载,精度高。此外,它还表明,该负荷模型的参数可以准确地确定了使用技术的建议。
目前的文件是有关理论和数值方面的鉴定累计模型的电力系统负荷。鉴定双方的线性和非线性模型的一个电力系统负荷是治疗。 2鉴定技术的介绍,属于那些所谓家庭的输出误差模型。首先,估计负荷参数采用线性模型,这是其次介绍了一种非线性识别技术。统计特性所建议的识别方法,研究了两个数值和分析。因此,人为制造的数据分析和数值的差异,所得到的估计是与相应的cramér -饶下界。然后,在以基准识别技术和验证分析负荷模型,实地测量所采取的在造纸厂使用。所取得的成果表明,该负荷模型描述的实际行为的负载,精度高。此外,它还表明,该负荷模型的参数可以准确地确定了使用技术的建议。
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