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

机译:有源配电网的状态估计:WLS与集成PMU的迭代卡尔曼滤波算法之间的比较

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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:快速(即,亚秒级)状态估计(SE)过程的发展代表了与有源分配网络(ADN)的实时控制有关的挑战性任务之一。众所周知,电力系统的SE问题将网络中执行的测量与一组非线性方程式链接起来,该非线性方程式表示网络节点电压相量(即系统状态)与测量量之间的链接。这些电压的计算是通过使用(例如)加权最小二乘(WLS)或卡尔曼滤波器(KF)方法解决最小化问题来完成的。相量测量单元(PMU)的特点是精度高,能够直接测量节点电压相量,从原则上讲可以简化SE问题。在此框架内,本文有两个目标。首先是基于迭代KF(IKF)的使用提出一种程序,旨在以简单的方式实现集成PMU测量的ADN SE。第二个目标是根据测量和过程协方差矩阵对WLS与IKF方法的性能进行敏感性分析。

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