scholarly journals Identifying DQ-Domain Admittance Models of a 2.3-MVA Commercial Grid-Following Inverter Via Frequency-Domain and Time-Domain Data

2021 ◽  
pp. 1-1
Author(s):  
Lingling Fan ◽  
Zhixin Miao ◽  
Przemyslaw Koralewicz ◽  
Shahil Shah ◽  
Vahan Gevorgian
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Time Domain Data

JOINT INTERPRETATION OF AIRBORNE ELECTROMAGNETIC DATA IN THE TIME DOMAIN AND FREQUENCY DOMAIN

2018 ◽  
Vol 12 (7-8) ◽  
pp. 76-83
Author(s):  
E. V. KARSHAKOV ◽  
J. MOILANEN
Keyword(s):  
Fourier Transform ◽  
Frequency Domain ◽  
Time Domain ◽  
Frequency Interval ◽  
Single Spectrum ◽  
Simultaneous Inversion ◽  
Homogeneous Half Space ◽  
Time Domain Data ◽  
The Time Domain

Тhe advantage of combine processing of frequency domain and time domain data provided by the EQUATOR system is discussed. The heliborne complex has a towed transmitter, and, raised above it on the same cable a towed receiver. The excitation signal contains both pulsed and harmonic components. In fact, there are two independent transmitters operate in the system: one of them is a normal pulsed domain transmitter, with a half-sinusoidal pulse and a small "cut" on the falling edge, and the other one is a classical frequency domain transmitter at several specially selected frequencies. The received signal is first processed to a direct Fourier transform with high Q-factor detection at all significant frequencies. After that, in the spectral region, operations of converting the spectra of two sounding signals to a single spectrum of an ideal transmitter are performed. Than we do an inverse Fourier transform and return to the time domain. The detection of spectral components is done at a frequency band of several Hz, the receiver has the ability to perfectly suppress all sorts of extra-band noise. The detection bandwidth is several dozen times less the frequency interval between the harmonics, it turns out thatto achieve the same measurement quality of ground response without using out-of-band suppression you need several dozen times higher moment of airborne transmitting system. The data obtained from the model of a homogeneous half-space, a two-layered model, and a model of a horizontally layered medium is considered. A time-domain data makes it easier to detect a conductor in a relative insulator at greater depths. The data in the frequency domain gives more detailed information about subsurface. These conclusions are illustrated by the example of processing the survey data of the Republic of Rwanda in 2017. The simultaneous inversion of data in frequency domain and time domain can significantly improve the quality of interpretation.

Effects of Noise, Time-Domain Damping, Zero-Filling and the FFT Algorithm on the “Exact” Interpolation of Fast Fourier Transform Spectra

1988 ◽  
Vol 42 (5) ◽  
pp. 715-721 ◽  
Author(s):  
Francis R. Verdun ◽  
Carlo Giancaspro ◽  
Alan G. Marshall
Keyword(s):  
Fourier Transform ◽  
Frequency Domain ◽  
Time Domain ◽  
Word Length ◽  
Peak Position ◽  
Double Precision ◽  
Time Domain Data ◽  
Roundoff Errors ◽  
Data Points ◽  
Lorentzian Spectrum

A frequency-domain Lorentzian spectrum can be derived from the Fourier transform of a time-domain exponentially damped sinusoid of infinite duration. Remarkably, it has been shown that even when such a noiseless time-domain signal is truncated to zero amplitude after a finite observation period, one can determine the correct frequency of its corresponding magnitude-mode spectral peak maximum by fitting as few as three spectral data points to a magnitude-mode Lorentzian spectrum. In this paper, we show how the accuracy of such a procedure depends upon the ratio of time-domain acquisition period to exponential damping time constant, number of time-domain data points, computer word length, and number of time-domain zero-fillings. In particular, we show that extended zero-filling (e.g., a “zoom” transform) actually reduces the accuracy with which the spectral peak position can be determined. We also examine the effects of frequency-domain random noise and roundoff errors in the fast Fourier transformation (FFT) of time-domain data of limited discrete data word length (e.g., 20 bit/word at single and double precision). Our main conclusions are: (1) even in the presence of noise, a three-point fit of a magnitude-mode spectrum to a magnitude-mode Lorentzian line shape can offer an accurate estimate of peak position in Fourier transform spectroscopy; (2) the results can be more accurate (by a factor of up to 10) when the FFT processor operates with floating-point (preferably double-precision) rather than fixed-point arithmetic; and (3) FFT roundoff errors can be made negligible by use of sufficiently large (> 16 K) data sets.

Inversion of airborne geophysics over the DO-27/DO-18 kimberlites — Part 2: Electromagnetics

2017 ◽  
Vol 5 (3) ◽  
pp. T313-T325 ◽  
Author(s):  
Dominique Fournier ◽  
Seogi Kang ◽  
Michael S. McMillan ◽  
Douglas W. Oldenburg
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Three Dimensions ◽  
Data Sets ◽  
Conductivity Model ◽  
Time Domain Data ◽  
Conductivity Structure ◽  
Lake Bottom ◽  
Geologic Interpretation ◽  
Lateral Extent

We focus on the task of finding a 3D conductivity structure for the DO-18 and DO-27 kimberlites, historically known as the Tli Kwi Cho (TKC) kimberlite complex in the Northwest Territories, Canada. Two airborne electromagnetic (EM) surveys are analyzed: a frequency-domain DIGHEM and a time-domain VTEM survey. Airborne time-domain data at TKC are particularly challenging because of the negative values that exist even at the earliest time channels. Heretofore, such data have not been inverted in three dimensions. In our analysis, we start by inverting frequency-domain data and positive VTEM data with a laterally constrained 1D inversion. This is important for assessing the noise levels associated with the data and for estimating the general conductivity structure. The analysis is then extended to a 3D inversion with our most recent optimized and parallelized inversion codes. We first address the issue about whether the conductivity anomaly is due to a shallow flat-lying conductor (associated with the lake bottom) or a vertical conductive pipe; we conclude that it is the latter. Both data sets are then cooperatively inverted to obtain a consistent 3D conductivity model for TKC that can be used for geologic interpretation. The conductivity model is then jointly interpreted with the density and magnetic susceptibility models from a previous paper. The addition of conductivity enriches the interpretation made with the potential fields in characterizing several distinct petrophysical kimberlite units. The final conductivity model also helps better define the lateral extent and upper boundary of the kimberlite pipes. This conductivity model is a crucial component of the follow-up paper in which our colleagues invert the airborne EM data to recover the time-dependent chargeability that further advances our geologic interpretation.

scholarly journals Time-resolving the COVID-19 outbreak using frequency domain analysis

2020 ◽  
Author(s):  
Keno L. Krewer ◽  
Mischa Bonn
Keyword(s):  
Steady State ◽  
Severe Acute Respiratory Syndrome ◽  
Spectral Density ◽  
Frequency Domain ◽  
Time Domain ◽  
Case Fatality ◽  
Frequency Domain Analysis ◽  
Time Domain Data ◽  
Current Outbreak ◽  
The Time Domain

AbstractDifficulties assessing and predicting the current outbreak of the severe acute respiratory syndrome coronavirus 2 can be traced, in part, to the limitations of a static description of a dynamic system. Fourier transforming the time-domain data of infections and fatalities into the frequency domain makes the dynamics easily accessible. Defining a quantity like the “case fatality” as a spectral density allows a more sensible comparison between different countries and demographics during an ongoing outbreak. Such a case fatality informs not only how many of the confirmed cases end up as fatalities, but also when. For COVID-19, knowing this time and using the entire case fatality spectrum allows determining that an outbreak had entered a steady-state (most likely its end) about 14 days before this is obvious from time-domain data. The lag between confirmations and deaths also helps to estimate the effectiveness of contact management: The larger the lag, the less time the average confirmed person had to infect people before quarantine.

Time and Frequency Domain Performance Comparison for Wheeze Detection Using K-Nearest Neighbor

2014 ◽  
Vol 591 ◽  
pp. 163-166
Author(s):  
Syamimi Mardiah Shaharum ◽  
Kenneth Sundaraj
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
High Performance ◽  
Nearest Neighbor ◽  
Dominant Frequency ◽  
Performance Comparison ◽  
K Nearest Neighbor ◽  
Time Domain Data ◽  
The Time Domain ◽  
Domain Performance

In this Paper, the Comparison between the Performance of Wheezes Data Processing in the Frequency Domain and in the Time Domain is Evaluated Using K-Nearest Neighbor (KNN). the Purpose of this Paper is to Clarify the Confusion Regarding the Methods Used Nowadays, as many of the Previous Researchers have Stated that Wheezes Data are Better Processed in the Frequency Domain due to its Dominant Frequency Peaks but Not a Single Researcher has Made a Direct Comparison to Prove the Reliability of the Method Used. from the Evaluation Made, the Result Shows that the Performance of Wheeze Data Processed in the Frequency Domain is Better as Compared to the Data Processed in the Time Domain. A High Performance Accuracy with 97% is Obtained Comparing to an Accuracy Percentage of 83.13% were only Achieved by Using the Time Domain Data. Thus, this Paper has Successfully Made a Comparison between the Domains Proving the Reliability of the Frequency Domain for Wheeze Detection.

scholarly journals ANALYSIS OF TIME DOMAIN MCSEM DATA IN THE PRESENCE OF RESISTIVE LAYERS

2012 ◽  
Vol 30 (3) ◽  
Author(s):  
Diego Da Costa Miranda ◽  
Cícero Roberto Teixeira Régis
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Theoretical Formulation ◽  
Resistive Layer ◽  
Hydrocarbon Reservoir ◽  
Time Domain Data ◽  
Electric Structure ◽  
The Time Domain ◽  
Almost All ◽  
Model Frequency

Modeling of mCSEM data is usually done in the frequency domain, from its theoretical formulation to the analysis of the results. However, the time domain approach is, in principle, capable of providing equivalent information about the geo-electric structure of the subsurface and has the advantage of recording only the secondary fields, so the signal is not dominated by the primary field. In this work, we model frequency domain mCSEM data in 1-D environments, thenwe perform the discrete Fourier transform to obtain time domain results. We simulated marine geological environments with and without the resistive layer that represents the hydrocarbon reservoir. We verified that, in almost all model configurations, the time domain data are significantly different when calculated for models withand without hydrocarbons. We calculated the results considering variations in the position of the receivers and in the sea depth. We observed the influence of the airwave, even at large sea depths, and although a simple separation of this influence on data is not possible, the time domain results allowed us to do a qualitative analysis of its effects on the survey.

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