Least square and Kalman based methods for dynamic phasor estimation: a review

Khodaparast, Jalal; Khederzadeh, Mojtaba

doi:10.1186/s41601-016-0032-y

Cited by 48 publications

(17 citation statements)

References 33 publications

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“…It can be concluded that the error occurring is above all the results of the introduced delay, similar to that of [15,21]. Owing to the modification made in calculating the parameters of the phasors, the magnitudes of this calculated error are smaller than in other methods based on the least squares method, with their size becoming comparable to [16,17,23,38], therefore being much smaller than [27,[39][40][41]. The proposed approach can calculate the derivatives of the phasor-phasor speed and acceleration, which reduces the estimation error.…”

Section: Simulation Resultssupporting

confidence: 65%

Dynamic Phasors Estimation Based on Taylor-Fourier Expansion and Gram Matrix Representation

Petrović

Damljanović

2018

Mathematical Problems in Engineering

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The paper presents a new approach to estimation of the dynamic power phasors parameters. The observed system is modelled in algebra of matrices related to its Taylor-Fourier-trigonometric series representation. The proposed algorithm for determination of the unknown phasors parameters is based on the analytical expressions for elements of the Gram’s matrix associated with this system. The numerical complexity and algorithm time are determined and it is shown that new strategy for calculation of Gram’s matrix increases the accuracy of estimation, as well as the speed of the algorithm with respect to the classical way of introducing the Gram’s matrix. Several simulation examples of power system signals with a time-varying amplitude and phase parameters are given by which the robustness and accuracy of the new algorithm are confirmed.

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Section: Simulation Resultssupporting

confidence: 65%

Dynamic Phasors Estimation Based on Taylor-Fourier Expansion and Gram Matrix Representation

Petrović

Damljanović

2018

Mathematical Problems in Engineering

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“…According to (11), we calculate the phasor matrix H of multi channels by a least square algorithm, as well. According to (10) and (11), the middle matrices have the size of (m × N ) − by − 2 where m is number of channels and N is number of sample per cycle.…”

Section: Generalized Multi-channel Pronymentioning

confidence: 99%

“…There are a number of research initiatives that investigate algorithms for phasor estimation [10], [11]. An adaptive filter is suggested in [12] to estimate phasors.…”

Section: Introductionmentioning

confidence: 99%

Real Time Phasor Estimation Based on Recursive Prony with Several Channels of One PMU

Khodaparast

Fosso

Molinas

2018

2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe)

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Phasor estimation is crucial for monitoring and control of smart power systems. The classical signal processing method named Prony has been used for estimating the parameters of measured signals such as frequency, damping factor and phasor. To reduce the impact of noise on the parameters estimated by Prony, multi-channel Prony has been previously explored and presented in the literature. The basic approach for multi-channel Prony is a generalized solution, in which new rows are added to matrices for every channel. Since the generalized multi-channel Prony is time-consuming, a new method based on recursive solution is proposed in this paper to make it suitable for real-time application. Here, several channels of one Phasor Measurement Unit (PMU) are used to estimate the phasor of current/voltage in a recursive pattern, in which the phasor is computed recursively over time based on previously calculated estimates and new measurements. The proposed method is compared with three other solutions for multi-channel Prony: a) data fusion which is based on the Kalman filter concept, b) an alternating direction method of multipliers (ADMM), and c) a consensus update approach which is based on an iterative procedure. Simulation results demonstrate the ability of the proposed method for real-time phasor estimation, both in terms of maintaining the accuracy and reducing computation time.

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“…The frequency and the rate of change of frequency are obtainable in this step. Finally, the amplitude and the phase are obtained by equation (2).…”

Section: First-order Prony Algorithmmentioning

confidence: 99%

“…Fast and precise estimation is also necessary for accurate decision in power system control. In the context of phasor estimation, there are several initiatives that investigate algorithms for phasor estimation [1], [2]. An adaptive filter is suggested in [3] to estimate phasors.…”

Section: Introductionmentioning

confidence: 99%

EMD-Prony for Phasor Estimation in Harmonic and Noisy Condition

Khodaparast

Fosso

Molinas

2018

2018 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM)

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Wide area monitoring systems (WAMSs) enables the real-time monitoring of power system dynamics by bringing together new developments in the field of measurement, communication and computing. Measurements of voltage and current phasors are recorded by phasor measurement units (PMUs) installed across a wide area power system and time tagged at the point of measurement using a common time reference. Estimates of Phasor, Frequency and rate of change of frequency (ROCOF) are duties of PMU in every installed bus. For this purpose, the Prony algorithm is one of the promising method since its phasor estimates are calculated adaptively based on estimated frequency. For fundamental phasor estimation, the Prony algorithm with the order of one is suitable but its performance in terms of accuracy is diminished when non-fundamental components interfere in the measured signal. These components can be eliminated from fundamental phasor estimates by increasing the Prony's model order. In order to specify adaptively the order of the Prony algorithm, the Empirical Mode Decomposition (EMD) method is proposed in this paper to be combined with the Prony algorithm as EMD-Prony. The EMD decomposes a signal into finite Intrinsic Mode Functions (IMF) based on the number of modes in the measured signal. The number of IMFs is utilized by Prony to extend its model to higher order and so purifies the fundamental phasor estimates. In addition, EMD is also used as a pre-processor to filter the noise from the input signal of Prony. Finally, the proposed method can estimate phasors accurately under noisy and harmonic conditions.

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Least square and Kalman based methods for dynamic phasor estimation: a review

Cited by 48 publications

References 33 publications

Dynamic Phasors Estimation Based on Taylor-Fourier Expansion and Gram Matrix Representation

Dynamic Phasors Estimation Based on Taylor-Fourier Expansion and Gram Matrix Representation

Real Time Phasor Estimation Based on Recursive Prony with Several Channels of One PMU

EMD-Prony for Phasor Estimation in Harmonic and Noisy Condition

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