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State estimation of Active Distribution Networks: Comparison between WLS and iterated kalman-filter algorithm integrating PMUs

机译:主动分配网络的状态估计:WLS与迭代卡尔曼滤波器算法的比较集成PMU

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One of the challenging tasks related to the realtime control of Active Distribution Networks (ADNs) is represented by the development of fast (i.e. sub-second) state estimation (SE) processes. As known, the problem of SE of power networks links the measurements performed in the network with a set of non-linear equations representing the links between the network node voltage phasors (i.e. the system states) and measured quantities. The calculation of these voltages is accomplished by the solution of a minimization problem by using, for instance, Weighted Least Squares (WLS) or Kalman filter (KF) methods. The availability of phasor measurement units (PMUs), characterized by high accuracy and able to directly measure node voltage phasors, allows, in principle, a simplification of the SE problem. Within this framework, the paper has two aims. The first is to propose a procedure based on the use of the Iterated KF (IKF) aiming at making achievable, in a straightforward manner, the SE of ADNs integrating PMU measurements. The second goal is to present a sensitivity analysis of the performances of WLS vs IKF methods as a function of the measurements and process covariance matrices.
机译:与活动分配网络(ADNS)的实时控制相关的具有挑战性的任务是由快速(即,子第二)状态估计(SE)过程的开发来表示的。如已知的,电力网络SE的问题链接在网络中执行的测量,其中一组非线性方程表示网络节点电压相位器(即系统状态)之间的链路和测量的数量。通过使用例如加权最小二乘(WLS)或卡尔曼滤波器(KF)方法,通过对最小化问题的解决方案来实现这些电压的计算。相分测量单元(PMU)的可用性,以高精度为特征,并且能够直接测量节点电压相片仪,原则上允许简化SE问题。在此框架内,本文有两个目标。首先是提出基于使用迭代的KF(IKF)的过程,以直接的方式实现旨在使得ADNS的SE集成PMU测量。第二个目标是呈现WLS与IKF方法的性能的敏感性分析,作为测量和过程协方差矩阵的函数。

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