Abstract

Phasor estimation under dynamic conditions has been under study recently by relaxing the amplitude and phase of the static phasor. This paper will review some methods to estimate dynamic phasor by nonlinear Kalman filters. Nonlinear Kalman filters have found an application in the tracking of time-varying amplitude and phase. Five nonlinear Kalman filtering methods for dynamic phasor estimation are examined in this paper. These methods are: EKF1 stands for first-order Extended Kalman filter, EKF2 stands for second-order Extended Kalman, UKF stands for Unscented Kalman filter, GHKF stands for Gauss Hermite Kalman filter, and finally CKF stands for Cubature Kalman filter. This paper describes the theoretical processes of these methods and demonstrates their effectiveness in dynamic phasor estimation by some test signal simulations in MATLAB. The simulation section shows that nonlinear Kalman filters give more accuracy than linear Kalman filters when the phasor is relaxed by modulated amplitude and phase. Moreover, comparative assessments among the performance of five nonlinear Kalman filters are done for dynamic phasor estimation, and also their performances are compared with the other methods which have already been published. According to the simulation results, EKF1 gives the highest accuracy during steady-state ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2 \times 10^{-13}$ </tex-math></inline-formula> ) because the signal model is more similar to the estimation model of EK1 during the steady-state condition. However, the other non-linear Kalman filters show better performances in dynamic conditions. When the phasor is a time-varying amplitude and phase, filters give the same accuracy ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$TVE=0.5 \%$ </tex-math></inline-formula> ). A step-change in amplitude and phase creates different overshoot and response times, but EKF2 shows the least overshoot (3.2%) and the longest response time (7.6 ms). Computation burden and noise indices can discriminate the methods from other viewpoints. The computation burden of GHKF is drastically increased when the number of states gives rise. CKF shows an appropriate performance when the number of samples and the number of the state increased in the input signal. EKF1 is not a good solution for noise infiltration when SNR is less than 40 dB, but CKF gives the highest accuracy in high noise levels. Compared to the six already pblished methods, CK shows the best performance with a reasonable estimation error ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$TVE=0.1101 \%$ </tex-math></inline-formula> ) and simulation time (0.6146 ms).

Highlights

  • T HE complexity of modern power systems is steadily increasing

  • Methods are selected from extended category (EKF1 and EKF2), black box category (UKF) and numerical integration solver (GHKF and Cubature Kalman filter (CKF)) to cover problem of dynamic phasor estimation from different viewpoints.This paper aims to investigate the performance of five different nonlinear Kalman filters for estimation of the dynamic phasor

  • This section analyzes the performances for five nonlinear Kalman filters for dynamic phasor estimation by different tests

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Summary

Introduction

T HE complexity of modern power systems is steadily increasing. It is inspiring for researchers to address several challenges of modern power systems. Power system dynamics vary significantly by huge penetrations of renewable energy, so it demands new platforms to monitor the power system. Phasor measurement units (PMUs) as a synchronized monitoring system presents a reliable platform for the complicated power system [1], [2]. Various signal analysis techniques can be employed to estimate applicable parameters of the measured signal by the PMU, such as Least square, Wavelet transform, Kalman filters, Hilbert-Huang Transform, and Prony [3]. There are two main standards for phasor estimation in phasor estimation unit (PMU) (IEC/IEEE 60255-118-1 published in 2018 and IEEE C37.118.1 published in 2011) that have constructed a framework and covered issues from phasor estimation to communication. Several dynamic test signals (such as modulation, frequency ramp, and signal parameters jumps) are recommended for the performance evaluation under dynamic conditions (where the signal parameters (i.e., amplitude, phase, and frequency)

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