A space vector based approach for synchrophasor measurement

Toscani, Sergio; Muscas, Carlo

doi:10.1109/i2mtc.2014.6860747

Cited by 23 publications

(21 citation statements)

References 8 publications

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“…Figures 4-7 present the error results of the RBF (marked as RBF-blue color), the quadrature filter method (marked as Met-green color), the permissible error values from standards (Stand-black color) and the results published in the literature [14][15][16][17]22,23,[26][27][28][39][40][41] (Lit-red color), respectively, for the P class of the PMU device in part (a) of each figure and for the M class of the PMU device in part (b) of each figure. The comparative form of the results presentation was applied.…”

Section: Resultsmentioning

confidence: 99%

Application of an Artificial Neural Network for Measurements of Synchrophasor Indicators in the Power System

2021

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Dynamic phenomena in electric power systems require fast and accurate algorithms for processing signals. The processing results include synchrophasor parameters, e.g., varying amplitude, phase or frequency of sinusoidal voltage or current signals. This paper presents a novel estimation method of synchrophasor parameters that comply with the requirements of IEEE/IEC standards. The authors analyzed an algorithm for measuring the phasor magnitude by means of a selected artificial neural network (ANN), an algorithm for estimating the phasor phase and frequency that makes use of the zero-crossing method. The original components of the presented approach are: the method of the synchrophasor magnitude estimation by means of a suitably trained and applied radial basic function (RBF); the idea of using two algorithms operating simultaneously to estimate the synchrophasor magnitude, phase and frequency that apply identical calculation methods are different in that the first one filters the input signal using the FIR filter and the second one operates without any filter; and the algorithm calculating corrections of the phase shift between the input and output signal and the algorithm calculating corrections of the magnitude estimation. The error results obtained from the applied algorithms were compared with those of the quadrature filter method and the ones presented in literature, as well as with the permissible values of the errors. In all cases, these results were lower than the permissible values and at least equal to the values found in the literature.

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Section: Resultsmentioning

confidence: 99%

Application of an Artificial Neural Network for Measurements of Synchrophasor Indicators in the Power System

2021

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“…Another technique that allows estimating both the positive sequence synchrophasor and the frequency has been proposed in [13].…”

Section: C) Space Vector (Sv) Methodsmentioning

confidence: 99%

“…Finally, in [13] an algorithm based on the space vector transformation of the phase quantities using a rotating reference frame and a proper filtering is proposed.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, some of the most recent synchrophasor measurement solutions, designed to comply with the Standard [1] requirements, are considered for an experimental comparative analysis. Results are reported for methodologies presented in [11]- [13] and for a methodology compliant with the P-class of the Standard [1] based on [6] and [15]. The validation of the considered approaches is performed by means of a suitable experimental platform.…”

Section: Introductionmentioning

confidence: 99%

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Experimental characterization of dynamic methods for synchrophasor measurements

Castello

Sulis

Toscani

2014

2014 IEEE International Workshop on Applied Measurements for Power Systems Proceedings (AMPS)

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This paper presents the results of an experimental characterization performed to assess the accuracy of custom algorithms developed for Phasor Measurement Units (PMUs). In particular, some of the most recent and efficient methodologies used for synchrophasors estimation under dynamic conditions are considered. The techniques are implemented in a modular measurement platform and are validated by means of test signals under both static and dynamic conditions defined in IEEE Standard C37.118.1. The comparison of the results obtained by means of simulations with those resulting from experimental tests is also reported in order to evaluate the impact of possible measurement hardware on the overall PMU accuracy

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“…Toscani [22] developed a PMU algorithm, using space vector transformation instead of IQ down-conversion, followed by filtering the real and imaginary parts of the resulting vectors using Bessel IIR filters. Toscani does not discuss the group delay requirement of the IIR filter.…”

Section: Related Workmentioning

confidence: 99%

A Phasor Measurement Unit Algorithm Using IIR Filters for FPGA Implementation

Kikkert

2019

Electronics

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Phasor measurement units (PMU) are increasingly used in electrical power transmission networks, to maintain stability and protect the network. PMUs accurately measure voltage, phase, frequency, and rate of change of frequency (ROCOF). For reliability, it is desirable to implement a PMU using an FPGA. This paper describes a novel algorithm, suited to implementation in an FPGA and based on a simple PMU block diagram. A description of its realization using low hardware complexity infinite impulse response (IIR) filters is given. The IEC/IEEE standard 60255-118-1:2018 Part 118-1: Synchrophasor measurements for power systems, describes "reference" Finite Impulse Response (FIR) filters for implementing PMU hardware. At the 10 kHz sampling frequency used for our implementation, each "reference" FIR filter requires 100 multipliers, while an 8th order IIR filter only requires 12 multipliers. This paper compares the performance of different order IIR filter-based PMUs with the performance of the same PMU algorithm using the IEC/IEEE FIR reference filter. The IIR-based PMU easily satisfies all the requirements of IEC/IEEE standard and has a much better out of band signal rejection performance than a FIR-based PMU. Steady state errors for a rated voltage ± 10% and a rated frequency ± 5 Hz are < 0.000001% for total vector error (TVE) and < 1 µHz for frequency, with a latency of two mains cycles.

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A space vector based approach for synchrophasor measurement

Cited by 23 publications

References 8 publications

Application of an Artificial Neural Network for Measurements of Synchrophasor Indicators in the Power System

Application of an Artificial Neural Network for Measurements of Synchrophasor Indicators in the Power System

Experimental characterization of dynamic methods for synchrophasor measurements

A Phasor Measurement Unit Algorithm Using IIR Filters for FPGA Implementation

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