Phasor estimation with finite impulse response notch filtering and half-cycle discrete fourier transform

2017 ◽  
Author(s):  
Jin Kwon Hwang
Keyword(s):  
Fourier Transform ◽  
Discrete Fourier Transform ◽  
Impulse Response ◽  
Finite Impulse Response ◽  
Half Cycle ◽  
Phasor Estimation ◽  
Notch Filtering

The Spectra of Filters

2021 ◽  
pp. 204-268
Author(s):  
Victor Lazzarini
Keyword(s):  
Fourier Transform ◽  
Impulse Response ◽  
Discrete Time ◽  
Filter Design ◽  
Finite Impulse Response ◽  
Infinite Impulse Response ◽  
Main Body ◽  
Analysis Tool ◽  
The Fourier Transform ◽  
Definition Of

This chapter now turns to the discussion of filters, which extend the notion of spectrum beyond signals into the processes themselves. A gentle introduction to the concept of delaying signals, aided by yet another variant of the Fourier transform, the discrete-time Fourier transform, allows the operation of filters to be dissected. Another analysis tool, in the form of the z-transform, is brought to the fore as a complex-valued version of the discrete-time Fourier transform. A study of the characteristics of filters, introducing the notion of zeros and poles, as well as finite impulse response (FIR) and infinite impulse response (IIR) forms, composes the main body of the text. This is complemented by a discussion of filter design and applications, including ideas related to time-varying filters. The chapter conclusion expands once more the definition of spectrum.

Design of optimized finite impulse response digital filters for use with passive Fourier transform infrared interferograms

1990 ◽  
Vol 62 (17) ◽  
pp. 1768-1777 ◽  
Author(s):  
Gary W. Small ◽  
Amy C. Harms ◽  
Robert T. Kroutil ◽  
John T. Ditillo ◽  
William R. Loerop
Keyword(s):  
Fourier Transform ◽  
Impulse Response ◽  
Digital Filters ◽  
Finite Impulse Response ◽  
Fourier Transform Infrared

PMU Phasor Estimation Using Different Techniques

2021 ◽  
pp. 70-99
Author(s):  
Hamid Bentarzi ◽  
Abderrahmane Ouadi
Keyword(s):  
Fourier Transform ◽  
Discrete Fourier Transform ◽  
Dynamic Models ◽  
Zero Crossings ◽  
Phasor Measurement ◽  
Input Signals ◽  
Measurement Units ◽  
Phasor Estimation ◽  
Phase Angles ◽  
True Values

Many models of phasor measurement units (PMU) have been implemented; however, few dynamic models have been developed when the power system parameters change. It is necessary to use a method that can somehow estimate the frequency and correct the phasors. The conventional way to determine frequency is to detect zero crossings per unit time. However, this method has many drawbacks such as high cost and low accuracy. Also, after the frequency determination, the phasor should be corrected by suitably modifying the algorithm without omitting any data. This chapter presents different estimation techniques such as discrete Fourier transform (DFT), smart discrete Fourier transform (SDFT) that may be used to estimate the phasors. These estimated values would be incorrect if the input signals are at an off-nominal frequency and the phase angles would drift away from the true values. To correct this issue, first of all, the off-nominal frequency has been estimated using different techniques such as least error squares and phasor measurement angle changing, and then it is used to correct the phasors.

Finite‐impulse‐response reduction‐to‐the‐pole filter

Geophysics ◽  
1998 ◽  
Vol 63 (6) ◽  
pp. 1958-1964 ◽  
Author(s):  
Richard S. Lu
Keyword(s):  
Fourier Transform ◽  
Frequency Response ◽  
Impulse Response ◽  
Fourier Transforms ◽  
Finite Impulse Response ◽  
Fir Filter ◽  
Data Set ◽  
Linear Convolution ◽  
Reduction To The Pole ◽  
Anomaly Map

Convolving a finite‐impulse‐response (FIR) filter with a magnetic anomaly map produces a reduction‐to‐the‐pole (RTP) that is superior to that of the conventional Fourier‐transform approach. The conventional approach, in which the map’s Fourier transform is multiplied by the frequency response of the RTP filter, is flawed by not accounting properly for the dimensions of the respective Fourier transforms. The resultant wraparound effect of circular convolution degrades the RTP map. The FIR filter, combined with linear convolution and appropriate choices for dimensions of data and filter, eliminates the wraparound effect, minimizes contamination of the result by noise, and improves stability. These properties are illustrated by a synthetic example and by application to an actual data set.

Analysis of Energy Loss-Gain Error in Discrete Fourier Transform

2014 ◽  
Vol 568-570 ◽  
pp. 172-175 ◽  
Author(s):  
Yao Lin Liu ◽  
Feng Han ◽  
Zhen Liu ◽  
Min Chen Zhai
Keyword(s):  
Fourier Transform ◽  
Energy Loss ◽  
Discrete Fourier Transform ◽  
Energy Difference ◽  
Half Cycle ◽  
Cycle Number ◽  
Conservation Of Energy ◽  
Rectangular Window ◽  
Continuous Signal ◽  
Gain Error

In asynchronous sampling, discrete Fourier transform (DFT) spectrum involves errors. Scholars have done great investigations on the correction techniques of DFT spectrum, but the errors have not been completely eliminated all along. In this paper, spectrums were examined from the principle of conservation of energy. It is unnoticed before that the energy of the digital signal, which is the analysis object of DFT, isn't equal to that of the finite continuous signal truncated by rectangular window. Thus the energy of their spectrums are different according to the Parseval's theorem. The Energy Loss-Gain (ELG) error was introduced to express the energy difference between these two spectrums. The ELG error is zero if the observed continuous signal is truncated in integral multiple of half cycle and it is related to the cycle number and sampling number in one cycle. Analysis show that the ELG error decreases with the increment of these two parameters, which are helpful to the engineering.

scholarly journals Precise Amplitude and Phase Determination Using Resampling Algorithms for Calibrating Sampled Value Instruments

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7345
Author(s):  
Yeying Chen ◽  
Enrico Mohns ◽  
Michael Seckelmann ◽  
Soeren de Rose
Keyword(s):  
Fourier Transform ◽  
Discrete Fourier Transform ◽  
Laboratory Experiments ◽  
Finite Impulse Response ◽  
Interpolation Method ◽  
Sampling Frequency ◽  
Signal Frequency ◽  
Mean Square ◽  
Calibration System ◽  
Phase Determination

Sampling-based calibration systems for calibrating “Sampled Value” (SV)-based instruments for substation automation require synchronised and time-aligned sampling processes. As the signal frequency of the power grid is always asynchronous to the standardised sampling frequencies according to IEC 61869-9, the sampled waveforms of the calibration system and of the SV-based device under test can be resampled to be synchronised and to allow better accuracy in the following measurements based on the Discrete Fourier Transform (DFT) of the resampled waveforms. The paper presents simulations and results for different resampling algorithms. A modified sinc interpolation method with a finite impulse response (FIR) is presented. The deviation of the results for the root mean square (RMS) and phase angle is in the order of 10−8V/V (or rad) for normalised frequencies of up to 20% of the sampling frequency. No practical degradation in the presence of noise and harmonics could be observed. In addition, laboratory experiments demonstrate the realization of the proposed resampling process in the future SV-based calibration systems for SV-based instrumentation.

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