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Real-Time Reduced Steady-State Model Synthesis of Active Distribution Networks Using PMU Measurements

机译:基于PMU测量的有源配电网实时简化稳态模型合成

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摘要

Due to the increase of generation sources in distribution networks, it is becoming very complex to develop and maintain models of these networks. Network operators need to determine reduced models of distribution networks to be used in grid management functions. This paper presents a novel method that synthesizes steady-state models of unbalanced active distribution networks with the use of dynamic measurements (time series) from phasor measurement units (PMUs). Since phasor measurement unit (PMU) measurements may contain errors and bad data, this paper presents the application of a Kalman filter technique for real-time data processing. In addition, PMU data capture the power system's response at different time-scales, which are generated by different types of power system events; the presented Kalman filter has been improved to extract the steady-state component of the PMU measurements to be fed to the steady-state model synthesis application. Performance of the proposed methods has been assessed by real-time hardware-in-the-loop simulations on a sample distribution network.
机译:由于配电网络中发电资源的增加,开发和维护这些网络的模型变得非常复杂。网络运营商需要确定要在网格管理功能中使用的简化的配电网络模型。本文提出了一种新颖的方法,该方法利用相量测量单元(PMU)的动态测量(时间序列)来合成不平衡有源配电网的稳态模型。由于相量测量单元(PMU)的测量可能包含误差和不良数据,因此本文介绍了卡尔曼滤波技术在实时数据处理中的应用。另外,PMU数据捕获电力系统在不同时间范围内的响应,这是由不同类型的电力系统事件生成的;对提出的卡尔曼滤波器进行了改进,以提取PMU测量值的稳态分量,以馈入稳态模型合成应用程序。已通过样本分发网络上的实时硬件在环仿真评估了所提出方法的性能。

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