Simultaneous Estimation of Moving-Vibration Parameters by Sliding Goertzel Algorithm in PLL Technique

Singh, Ksh. Milan

doi:10.1109/tim.2018.2844898

Cited by 11 publications

(4 citation statements)

References 23 publications

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“…The k th computed frequency is obtained by mean of Eq. 14and (16) where the complexity reduction of the sequence a (p) is elaborated in the next section for radices r = 2, 4 and 8.…”

Section: The First and Second Order Jm-filtermentioning

confidence: 99%

“…Signal monitoring is an expanding domain that deals with the detection of any abrupt changes in a specific known frequency, such as fault detection machine, or to scan a preselected set of frequencies [1], the recognition of the dual-tone multi-frequency (DTMF) signaling [3] [4], as in radiofrequency identification (RFID) tags [5], spectrum analyzer based on sliding transforms [6] [7], in wireless communication with the orthogonal frequency division multiplex (OFDM) [8], channelizer [9], and modulated filter banks [10] where low complexity, low power consumption and high throughput are required, in the detection of coding regions in large DNA sequences [11], [12] and [13], and other domains such as the detection of any abrupt changes in a specific known frequency, deployed in resonance frequency estimation to tune notch filter in grid converter [14], in phase lock loop (PLL) [15] [16], in induction motor fault detection by space vector angular fluctuation [17], in real-time (remote/in-situ) detection and identification of physical properties and trace chemical/bio substances in industrial and other environments and cracks detection of structural surface [18]. More recently, many other applications on power electronics to monitoring component and smart grid power quality [19][20] [21].…”

Section: Introductionmentioning

confidence: 99%

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The JM-Filter to Detect Specific Frequency in Monitored Signal

Jaber

Massicotte

2021

IEEE Trans. Signal Process.

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The Discrete Fourier Transform (DFT) is a mathematical procedure that stands at the center of the processing inside a Digital Signal Processor (DSP). It has been widely known and argued in relevant literature that the Fast Fourier Transform (FFT) is useless in detecting specific frequencies in a monitored signal of length N because most of the computed results are ignored. In this paper, we present an efficient FFT-based method to detect specific frequencies in a monitored signal, which will then be compared to the most frequently used method which is the recursive Goertzel algorithm that detects and analyses one selectable frequency component from a discrete signal. The proposed JM-Filter algorithm presents a reduction of iterations compared to the first and second order Goertzel algorithm by a factor of r, where r represents the radix of the JM-Filter. The obtained results are significant in terms of computational reduction and accuracy in fixed-point implementation. Gains of 15 dB and 19 dB in signal to quantization noise ratio (SQNR) were respectively observed for the proposed first and second order radix-8 JM-Filter in comparison to Goertzel algorithm.

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“…The k th computed frequency is obtained by mean of Eq. 14and (16) where the complexity reduction of the sequence a (p) is elaborated in the next section for radices r = 2, 4 and 8.…”

Section: The First and Second Order Jm-filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The JM-Filter to Detect Specific Frequency in Monitored Signal

Jaber

Massicotte

2021

IEEE Trans. Signal Process.

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“…The sliding integrations from t to as mentioned in ( 4) and ( 5) can be carried out numerically in a discrete sampled system. The method applied for obtaining and updating the numerical integration over one cycle upon picking each fresh sample of xi (or yi) is to insert a delaying buffer of length after the discrete time integrator [15]. Thus, integration over is updated every new sample of xi and it is implemented as shown schematically in Figure . In this implementation, the buffer is initialized with zeroes and it takes / samples to be filled incrementally with valid readings as is equal to the sampling period.…”

Section: Sliding Ft Implementation (Sft)mentioning

confidence: 99%

Frequency adaptive sliding fourier transform for synchronizing VSI to the grid

Arafa

Abdallah

Aziz

2019

IJPEDS

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Sliding Discrete Fourier Transform (SDFT) is very efficient regarding computational load and it possesses a very fast phase angle detection with excellent harmonic rejection at nominal frequency. However, at off-nominal frequency, SDFT generates errors in both magnitude and phase angle due to spectral leakage. This paper introduces a workaround for Fourier Transform to handle this disability under off-nominal frequency while avoiding variable-rate sampling. Sliding Fourier Transform (SFT) is used as a phase detector for a phase-locked loop whose output frequency is used to drive the SFT. The paper revisits the mathematics of Fourier Transform (FT) in a three-phase setting via a time-domain approach to show a newly proposed filtering technique for the double-frequency oscillation just by summing the FT sine/cosine filter outputs of the three individual phases. Also, the analysis aims to determine and correct the phase and magnitude errors under offnominal frequency operation. The proposed technique (SFT-PLL) is tested in real time on dSPACE DS1202 DSP using voltage vectors that are pregenerated to simulate the most adverse grid conditions. The testing scenarios compare the performance of the SFT-PLL with that of the Decoupled Stationary Reference Frame PLL (dαβPLL). The results prove that SFT-PLL is superior to dαβPLL.

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“…These algorithms are applied on a sampleby-sample basis to analyze the time-frequency spectrum of the desired signals. Recently, SDFT has been used in various applications, e.g., acoustic echo cancellation [9], vibration mode estimators [10], [11], and in defect detection in the rotor bars of an induction motor [12]. The SDFT algorithm directly uses the previous DFT bins to iteratively compute each new output bin, thus making the SDFT's…”

Section: Introductionmentioning

confidence: 99%

A Non-Feedback-Loop and Low-Computation- Complexity Algorithm Design for a Novel 2-D Sliding DFT Computation

Juang

Lai

2019

IEEE Access

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This paper presents a forward-path, novel, two-dimensional (2-D) sliding discrete Fourier transform (SDFT) algorithm based on the column-row 2-D DFT concept and the shifted window property. After applying a descending dimension method (DDM), a mixed-radix and butterfly-based structure can be further employed to effectively implement the proposed algorithm. Conceptually, it has many advantages, including greater stability, more accuracy, and less computational complexity because there are no extra feedback loops in the calculation. The evaluation results are based on the following conditions: (1) the window size (N ) to 16 × 16; (2) the test pattern is an SVC grayscale video, and the formats are CIF and 4CIF with 30fps; (3) one multiplication involves four real multiplications and two real additions. The proposed 2-D SDFT method clearly reduced the number of multiplications by 43.8% and only increased the number of additions by 33%, compared with the state-of-the-art Park's method. Additionally, for the first 100 frames of the CIF and 4CIF sequences, the proposed method saves 10.8% and 10.9% of the processing time, respectively, on average. Overall, the proposed DDM-based 2-D SDFT algorithm can be applied to calculate not only 1-D but also 2-D SDFT spectrum, and are especially appropriate for hybrid applications. INDEX TERMSSliding discrete Fourier transform (SDFT), two-dimension SDFT (2-D SDFT), column-row 2-D DFT, descending dimension method (DDM), DDM-based 2-D SDFT.

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Simultaneous Estimation of Moving-Vibration Parameters by Sliding Goertzel Algorithm in PLL Technique

Cited by 11 publications

References 23 publications

The JM-Filter to Detect Specific Frequency in Monitored Signal

The JM-Filter to Detect Specific Frequency in Monitored Signal

Frequency adaptive sliding fourier transform for synchronizing VSI to the grid

A Non-Feedback-Loop and Low-Computation- Complexity Algorithm Design for a Novel 2-D Sliding DFT Computation

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