Power measurement in the non-coherent sampling

Agrež, D.

doi:10.1016/j.measurement.2006.12.005

Cited by 17 publications

(14 citation statements)

References 8 publications

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“…The measurement (sampling) time T M = N T S (T S being the sampling interval) can also be expressed as T M = (M + λ) T SIG , where integer M is an integer number of sampled periods (representing the number of signal periods T SIG sampled completely) and λ is the decimal part of the last sampled signal period (0 ≤ λ< 1). There is P' = P for λ = 0 (coherent sampling), and P' ≠ P for λ ≠ 0 [4,5]. Since exactly coherent sampling is usually not possible (e.g.…”

Section: Investigated Active Power Estimation Methodsmentioning

confidence: 99%

“…2 is based on measurements using the low-cost National Instruments PCI 6023E multiplexing DAQ board. The curves for simultaneous sampling and for spline interpolation overlap in the part of The effect of non-simultaneous channel sampling can be eliminated by using DAQ plug-in boards with simultaneous channel sampling with a separate ADC (nowadays usually of sigma-delta type) in each channel (the authors of this paper used a PCI 4472 DAQ board), or by using synchronized high-accuracy digital voltmeters for each signal channel sampling, as in [5]. The fundamental cause of the active power uncertainty component corresponding to the noise in the data acquisition channels is the quantization noise in each channel.…”

Section: Investigated Active Power Estimation Methodsmentioning

confidence: 99%

“…The fundamental cause of the active power uncertainty component corresponding to the noise in the data acquisition channels is the quantization noise in each channel. This can also be expressed by means of the signal-to-noise ratio (SNR) [5].…”

Section: Investigated Active Power Estimation Methodsmentioning

confidence: 99%

“…This method is not mentioned either in [4] or in [5]. The effect of non-coherent signal sampling can also be reduced by interpolating the sequence of signal samples (i.e.…”

Section: Power Estimation Using Interpolation and Re-sampling In The mentioning

confidence: 99%

“…In active power estimation, the DC component of the instantaneous power spectrum has to be found. Its fractional frequency bin is zero, and simplified interpolation expressions can therefore be used [5]. If a cosine window w(n) of order L and of length N defined by (3) is used,…”

Section: Power Estimation In the Frequency Domain Using Windowing Andmentioning

confidence: 99%

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Active Power Measurements - An Overview and a Comparison of DSP Algorithms by Noncoherent Sampling

Sedláček

Stoudek²

2011

Metrology and Measurement Systems

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This paper presents an overview of algorithms for one-phase active power estimation using digital signal processing in the time domain and in the frequency domain, and compares the properties of these algorithms for a sinusoidal test signal. The comparison involves not only algorithms that have already been published, but also a new algorithm. Additional information concerning some known algorithms is also included. We present the results of computer simulations in MATLAB and measurement results gained by means of computer plug-in boards, both multiplexed and using simultaneous signal sampling. The use of new cosine windows with a recently published iterative algorithm is also included, and the influence of additive noise in the test signal is evaluated.

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Section: Investigated Active Power Estimation Methodsmentioning

confidence: 99%

Section: Investigated Active Power Estimation Methodsmentioning

confidence: 99%

Section: Investigated Active Power Estimation Methodsmentioning

confidence: 99%

“…This method is not mentioned either in [4] or in [5]. The effect of non-coherent signal sampling can also be reduced by interpolating the sequence of signal samples (i.e.…”

Section: Power Estimation Using Interpolation and Re-sampling In The mentioning

confidence: 99%

Section: Power Estimation In the Frequency Domain Using Windowing Andmentioning

confidence: 99%

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Active Power Measurements - An Overview and a Comparison of DSP Algorithms by Noncoherent Sampling

Sedláček

Stoudek²

2011

Metrology and Measurement Systems

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The Precise Electric Energy Measurement Method Based on Modified Compound Simpson Integration

Zhang

Wei

Feng

2013

Lecture Notes in Electrical Engineering

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Hanning self-convolution window and its application to harmonic analysis

Wen

Teng

Guo

et al. 2009

Sci. China Ser. E-Technol. Sci.

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The Hanning self-convolution window (HSCW) is proposed in this paper. And the phase difference correction algorithm based on the discrete spectrum and the HSCW is given. The HSCW has a low peak side lobe level, a high side lobe roll-off rate, and a simple spectrum representation. Hence, leakage errors and harmonic interferences can be considerably reduced by weighting samples with the HSCW, the parameter estimation by the HSCW-based phase difference correction algorithm is free of solving high order equations, and the overall method can be easily implemented in embedded systems. Simulation and application results show that the HSCW-based phase difference correction algorithm can suppress the impacts of fundamental frequency fluctuation and white noise on harmonic parameter estimation, and the HSCW is advantageous over existing combined cosine windows in terms of harmonic analysis performance.hanning self-convolution window, FFT, phase difference, spectral leakage, frequency fluctuationThe accurate dynamic estimation of signal parameters has been a hot topic in the field of signal processing [1] . The estimation can provide an information basis for power measurment [2] , fault diagnosis [3] , electrical harmonic compensation [4] , and orthogonal frequency-division multiplexing [5] . Compared with wavelet transforms, the fast Fourier transform (FFT) is more computationally efficient and easier for implementations in embedded systems such as DSP and ARM, and is by now the most widely used in various signal parameter estimation algorithms [6,7] .For dynamic signals, it is difficult to achieve strict synchronous sampling even if frequency tracking technologies are adopted [8,9] . When using FFT to estimate signal parameters with asynchronous sampling, estimation error due to the spectral leakage and picket fence effect introduced by the asynchronous sampling and signal cutoff is relatively large [10] . Various kinds of windows, e.g., the rectangular window [11] , the Hanning window [12] , the Hamming window [13] , the Blackman window [14] , the Blackman-Harris window [15] , the RifeVincent window [16] , the Nuttall window [17] , the polynomial windows [18] , the flat-top window [19] , and the rectangular convolution window [20] , have been proposed and used in the windowed interpolation FFT algorithms, and they can in some degree suppress spectral leakage and increase the accuracy of the signal parameter estimation. The use of the FFT algorithms with dual-spectrum-line [7,12,14] or multi-spectrum-line [6,8] interpolation based on high order combined cosine windows in fundamental and harmonics parameter estimation involves, however, solving high order equations, which is computationally expensive [21][22][23] . Different approaches have been proposed to solve the problem. Yang, Ding et al. gave a discrete phase difference correction algorithm suitable for all kinds of symmetrical windows, which can calculate signal parameters without relying on the expression of the window spectral function [24] . Yang et al. proposed ...

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Power measurement in the non-coherent sampling

Cited by 17 publications

References 8 publications

Active Power Measurements - An Overview and a Comparison of DSP Algorithms by Noncoherent Sampling

Active Power Measurements - An Overview and a Comparison of DSP Algorithms by Noncoherent Sampling

The Precise Electric Energy Measurement Method Based on Modified Compound Simpson Integration

Hanning self-convolution window and its application to harmonic analysis

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