Real-Time Estimation of Power System Frequency Using  a Three-Level Discrete Fourier Transform Method

Nam, Soon-Ryul; Kang, Seung-Hwa; Kang, Sang-Hee

doi:10.3390/en8010079

Cited by 29 publications

(37 citation statements)

References 48 publications

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“…Accurately measuring the frequency of a power system is important for evaluating the stability of the system. (1)(2)(3)(4)(5)(6) At present, many techniques have been proposed for measuring the power system frequency. Common techniques include frequency domain interpolation, Prony's method, (7,8) the zero crossing method, (9,10) discrete Fourier transform (DFT) or fast Fourier transform (FFT), (1,3,6) and so on.…”

Section: Introductionmentioning

confidence: 99%

Curve Fitting Approach for Fundamental Frequency Measurement for Power Systems

Lee¹,

Huang²,

Men-Shen³

2020

Sensors and Materials

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In recent years, owing to an increase in electrical energy consumption, the quality and safety issues of power systems have drawn considerable attention. The frequency of a power system is an important indicator of its stability. The system frequency will drop when large generator sets are tripped. Therefore, it is important to measure the fundamental frequency of a power system accurately. The power system frequency can be estimated through various methods in time or frequency domains. Among these methods, curve fitting is a time domain approach that can be used to identify the parameters of a model using the input information obtained by utilizing a nonlinear regression method to fit the input curve. The physical phenomenon of a power system is described by a mathematical model. The curve fitting approach is applied to find the parameters such that the model is closer to the measured signal. These parameters are used to obtain the fundamental frequency of the power system. In this paper, the genetic algorithm (GA) is compared with the conventional regression analysis (RA) method for identifying the parameters of the model. The performance of curve fitting using different mathematical models on various power system events is discussed.

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Section: Introductionmentioning

confidence: 99%

Curve Fitting Approach for Fundamental Frequency Measurement for Power Systems

Lee¹,

Huang²,

Men-Shen³

2020

Sensors and Materials

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“…Spectral techniques compensate for this leakage error to estimate the fundamental frequency. Orthogonal techniques [6][7][8][9][10][11][12][13][14] are another of the most popular approaches for real-time applications. In this approach, filters generate orthogonal signals and complex vectors, which are used to estimate the fundamental frequency.…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Algorithm to Estimate the Fundamental Frequency of Asynchronously Sampled Signals in Power Systems

et al. 2015

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A two-stage algorithm is proposed for the estimation of the fundamental frequency of asynchronously sampled signals in power systems. In the first stage, time-domain interpolation reconstructs the power system signal at a new sampling time and the reconstructed signal passes through a tuned sine filter to eliminate harmonics. In the second stage, the fundamental frequency is estimated using a modified curve fitting, which is robust to noise. The evaluation results confirm the efficiency and validity of the two-stage algorithm for accurate estimation of the fundamental frequency even for asynchronously sampled signals contaminated with noise, harmonics, and an inter-harmonic component.

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“…They also capture the data using a specific sampling frequency, which is selectable in many cases. The length of the measured time [3], i.e., the time window t w to be displayed, is given by Equation (8), where N is the number of samples and T s is the sampling period. The sampling period can be related with the sampling frequency f s [3] using Equation (9).…”

Section: Acquisition Of a Signal Time-lapsementioning

confidence: 99%

“…This is achieved by using the MATLAB functions rM, Is " maxpXq and rM, Is " minpXq, where X is a discrete sequence, M is the maximum or minimum value (according to the function used) and I is the index where the value of interest is located. (7) The signal period, the signal frequency and the number of samples necessary to represent a single cycle are approximated by using Equations (10), (11) and (13), where the amount of time that needs to be displayed is the period of the signal; i.e., t w " T. (8) In this step, a simulation of the scan backlog effect is considered. The scan backlog indicates how much data remain in the buffer after each retrieval, providing a measure of how well the application is maintaining the throughput rate [20,23]; i.e., a Data Acquisition Card (DAQ) does not retrieve the data at the same rate as the sampling frequency f s .…”

Section: Automatic Fourier Spectrum Detection Using Autocorrelationmentioning

confidence: 99%

“…In former studies, the zero-crossing method has been used as one of the simplest ways to estimate the power system frequency; however, the measured signals typically contain harmonic distortions, which may introduce significant errors when using this method [8]. In some practical applications, the autocorrelation function [9][10][11] is used to identify the periodicities in an observed physical signal, which may be corrupted by a random interference [9].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Frequency Identification under Sample Loss in Sinusoidal Pulse Width Modulation Signals Using an Iterative Autocorrelation Algorithm

Said

Davizón

Román³

et al. 2016

Symmetry

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Abstract:In this work, we present a simple algorithm to calculate automatically the Fourier spectrum of a Sinusoidal Pulse Width Modulation Signal (SPWM). Modulated voltage signals of this kind are used in industry by speed drives to vary the speed of alternating current motors while maintaining a smooth torque. Nevertheless, the SPWM technique produces undesired harmonics, which yield stator heating and power losses. By monitoring these signals without human interaction, it is possible to identify the harmonic content of SPWM signals in a fast and continuous manner. The algorithm is based in the autocorrelation function, commonly used in radar and voice signal processing. Taking advantage of the symmetry properties of the autocorrelation, the algorithm is capable of estimating half of the period of the fundamental frequency; thus, allowing one to estimate the necessary number of samples to produce an accurate Fourier spectrum. To deal with the loss of samples, i.e., the scan backlog, the algorithm iteratively acquires and trims the discrete sequence of samples until the required number of samples reaches a stable value. The simulation shows that the algorithm is not affected by either the magnitude of the switching pulses or the acquisition noise.

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Real-Time Estimation of Power System Frequency Using a Three-Level Discrete Fourier Transform Method

Cited by 29 publications

References 48 publications

Curve Fitting Approach for Fundamental Frequency Measurement for Power Systems

Curve Fitting Approach for Fundamental Frequency Measurement for Power Systems

A Two-Stage Algorithm to Estimate the Fundamental Frequency of Asynchronously Sampled Signals in Power Systems

Automatic Frequency Identification under Sample Loss in Sinusoidal Pulse Width Modulation Signals Using an Iterative Autocorrelation Algorithm

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