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Particle Filter Based State Estimation of Power System

机译:基于粒子滤波的电力系统状态估计

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摘要

State estimation is very important for the analysis of power system. It helps for controlling and monitoring of the power system. With the help of it optimal state of the power system can be obtained. It measures the voltage and current phasors and also measure the angles between buses. So estimation of the power system should be more accurate. Power system is highly non-linear so it is not an easy task to forecast the state of the power system with the methods used before. Particle filter is very obvious solution for these type of non-linear system. It gives very accurate results of the estimations of the system. RMS error due to particle filter between actual and estimated state of the system is very low in compare to conventional estimators. Here for the estimation purpose we have used data of the six bus system and tried to estimate the voltage for the bus number one.
机译:状态估计对于电力系统的分析非常重要。它有助于控制和监视电源系统。借助于它,可以获得电力系统的最佳状态。它可以测量电压和电流相量,还可以测量母线之间的角度。因此,对电力系统的估算应该更加准确。电力系统是高度非线性的,因此使用以前使用的方法来预测电力系统的状态并非易事。对于这些类型的非线性系统,粒子滤波器是非常明显的解决方案。它给出了非常准确的系统估计结果。与常规估计器相比,由于粒子滤波器导致的系统实际状态与估计状态之间的RMS误差非常低。在这里,出于估算的目的,我们使用了六总线系统的数据,并试图估算第一总线的电压。

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