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Control Strategies for Active Filters
Published in L. Ashok Kumar, S. Albert Alexander, Computational Paradigm Techniques for Enhancing Electric Power Quality, 2018
L. Ashok Kumar, S. Albert Alexander
A fast Fourier transform (FFT) is used to compute the discrete Fourier transform (DFT) and its inverse. A Fourier transform converts functions from time to frequency domains and vice versa. FFT computes such transformations by factorizing the DFT matrix into a product of sparse factors. In DSTATCOMs, FFT is used to extract the harmonic components from the harmonic polluted signals. Owing to excessive computation in on-line application of FFT, it has high response time [90,91]. Generally, in order to detect current harmonics and voltage sags, the conventional detection algorithms such as RMS-based algorithm and FFT-based algorithm have been introduced to achieve the detection task. In this paper [92], an alternative algorithm, the adaptive predictive algorithm, is adopted to do the same task as the conventional algorithms. The salient feature of the adaptive algorithm is that it can detect the event as fast as the conventional algorithm can. Thus, it would be appropriate to detect events like voltage sags, because voltage sags of even very short duration may result in malfunction to sensitive loads.
DFT and Sampled Signals
Published in Eleanor Chu, Discrete and Continuous Fourier Transforms, 2008
Since the inverse DFT matrix is the complex conjugate of the (symmetric) DFT matrix, the command M_inv = conj(dft2_matrix(N)) produces the inverse. Alternatively, one may choose to output both matrices as shown in the modi ed listing below. Note that we have added the second output argument M_inv.
Hardware chip performance analysis of different FFT architecture
Published in International Journal of Electronics, 2021
Amit Kumar, Adesh Kumar, Aakanksha Devrari
The discrete Fourier transform (DFT) is a widely used tool in several applications of digital signal processing (DSP) systems. It has a vital role in several applications such as signal analysis, speech processing, image processing, audio processing, video processing, communication systems and many others. DFT is a Fourier representation of signal over finite length sequence. The DFT is achieved by decomposing the valued sequences into different frequency components. It converts time domain signal to a frequency domain signal for the same length while IDFT converts frequency domain signal to time domain signal. The FFT (Cooley & Tukey, 1965) is an algorithm used to compute ‘N’ point DFT of a sequence while inverse FFT is used to compute IDFT. The FFT computes fast by factorising the DFT matrix into a product of sparse factors, mostly zero. An FFT can easily reduce the complexity of DFT hardware. The brute-force calculation of ‘N’ length DFT requires O(N2) multiplications, whereas FFT can reduce the complexity from O(N2) to O(N log2N) for a DFT of length ‘N’. The general equation of DFT for input sequence x(n) over a length ‘N’ is given by
Feature Extraction and Classification Techniques for Power Quality Disturbances in Distributed Generation: A Review
Published in IETE Journal of Research, 2023
Nivedita Singh, M.A. Ansari, Manoj Tripathy, Vivek Pratap Singh
By factorizing the DFT matrix into a set of sparse (mostly zero) variables, an FFT easily calculates these transformations [22]. Mathematically, FFT can be represented as follows: The inverse FFT can be calculated as in the following equation: