Voltage phasor and local system frequency estimation using Newton type algorithm

Terzija, Vladimir; Djurić, M.; Kovačevíc, Branko

doi:10.1109/61.311162

Cited by 268 publications

(93 citation statements)

References 10 publications

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“…A lot of methods with regards to frequency estimation such as the techniques that have been published in the literature [34]- [37] exist. But most of them require high computational burden and complex implementation.…”

Section: Frequency Estimation Algorithmmentioning

confidence: 99%

Phasors Estimation at Offnominal Frequencies Through an Enhanced-SVA Method With a Fixed Sampling Clock

Ghafari

Almasalma

Raison

et al. 2017

IEEE Trans. Power Delivery

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Abstract--This paper provides a solution to estimate synchrophasors with high accuracy at off-nominal frequencies using a nominal frequency clock. The sampling rate is fixed, while the amplitude of the sampled values is altered to generate a sequence of samples whose sampling frequency is synchronized to the power system frequency. The re-computation of the synchronized samples is done by cubic spline interpolation. The algorithm requires knowledge of the fundamental frequency of the power system which is estimated based on the phase angle difference between two phasors. The performance of the algorithm has been tested by simulation, through different scenarios, following the test conditions described in the IEEE Standard C37.118.1-2014a. The test cases include: steady-state with off-nominal frequencies, harmonic distortions, amplitude and phase modulations, positive and negative ramps of frequency, and frequency jump. The simulation results show the technique's capability in efficiently dealing with harmonics and off-nominal frequencies under static and dynamic conditions.

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Section: Frequency Estimation Algorithmmentioning

confidence: 99%

Phasors Estimation at Offnominal Frequencies Through an Enhanced-SVA Method With a Fixed Sampling Clock

Ghafari

Almasalma

Raison

et al. 2017

IEEE Trans. Power Delivery

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show abstract

“…7, we can estimate the frequency iteratively by resetting the matrix A with different estimated frequency f'. In reality, sampling rate is set properly to fulfill the requirement of Nyquist's rule and the criterion of sampling in Terzija et al (1994). Secondly, an over-determined linear set is built to give a more precise estimation with more samples.…”

Section: Methodsmentioning

confidence: 99%

“…In the past several decades researchers have paid much attention to frequency measurement and analysis in power engineering. Types of frequency estimation methods have been reported, such as zero crossing (Begovic et al, 1993;Nguyen and Srinivasan, 1984), demodulation technique (Begovic et al, 1993), Newton algorithm (Terzija et al, 1994), Kalman filter (Wood et al, 1985;Routray et al, 2002;Siavashi et al, 2009), prony approach (Lobos and Rezmer, 1997), artificial neural network (Vianello et al, 2010), etc. in time-domain.…”

Section: Introductionmentioning

confidence: 99%

A Method of Least Squares Error with Iterative Resampling for Frequency Tracking in Smart Grid

Li¹,

Cao²,

Yuan³

2015

J. of Applied Sciences

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A B S T R A C T Phase angle and amplitude of a phasor can be provided by method of Least Squares Error (LSE). The LSE adopted iteratively is able to track the frequency and amplitude of power system in steady states and in kinds of non-steady ones. Iterative LSE with resampling is analyzed in conditions of off-nominal input, nominal input with harmonics and decaying Direct Current (DC) offset and additive Gaussian white noise. In the circumstances of frequency and phase step changes, performance of resampling LSE is compared with traditional LSE. Resampling LSE has better performance than the traditional one in frequency tracking ability and can provide less mean square error.

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“…However, its performance is prone to error in unbalanced conditions. A variety of approaches, including adaptive neural networks (ANN) [8,9], least square techniques [10][11][12], Newton-type algorithms [13], Prony estimation [14], DFTs [15][16][17], and the maximum likelihood method [18,19] have also been used to estimate the frequency of power system signals.…”

Section: Introductionmentioning

confidence: 99%

“…Several methods for frequency estimation have been reported in the technical literature [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Zero-crossingbased techniques are the most widely used methods for frequency estimation [1,2].…”

Section: Introductionmentioning

confidence: 99%

Fundamental Frequency Estimation in Power Systems Using Complex Prony Analysis

Nam¹,

Lee²,

Kang³

et al. 2011

Journal of Electrical Engineering and Technology

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-A new algorithm for estimating the fundamental frequency of power system signals is presented. The proposed algorithm consists of two stages: orthogonal decomposition and a complex Prony analysis. First, the input signal is decomposed into two orthogonal components using cosine and sine filters, and a variable window is adapted to enhance the performance of eliminating harmonics. Then a complex Prony analysis that is proposed in this paper is used to estimate the fundamental frequency by approximating the cosine-filtered and sine-filtered signals simultaneously. To evaluate the performance of the algorithm, amplitude modulation and harmonic tests were performed using simulated test signals. The performance of the algorithm was also assessed for dynamic conditions on a singlemachine power system. The Electromagnetic Transients Program was used to generate voltage signals for a load increase and single phase-to-ground faults. The performance evaluation showed that the proposed algorithm accurately estimated the fundamental frequency of power system signals in the presence of amplitude modulation and harmonics.

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Voltage phasor and local system frequency estimation using Newton type algorithm

Cited by 268 publications

References 10 publications

Phasors Estimation at Offnominal Frequencies Through an Enhanced-SVA Method With a Fixed Sampling Clock

Phasors Estimation at Offnominal Frequencies Through an Enhanced-SVA Method With a Fixed Sampling Clock

A Method of Least Squares Error with Iterative Resampling for Frequency Tracking in Smart Grid

Fundamental Frequency Estimation in Power Systems Using Complex Prony Analysis

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