Static Characterization of Arbitrary Waveform Generator based on Modified Multi- sine Fitting and Zero Crossing Detection Algorithms

Carnì, Domenico Luca

doi:10.25103/jestr.103.03

Cited by 4 publications

(3 citation statements)

References 16 publications

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“…The filtered samples are processed by a multi-sine fitting algorithm. This is a non-iterative procedure that estimates the parameters of the fundamental and harmonic components by minimizing the RMS error between the samples received in the input and the reconstructed ones [ 52 ]. In this way, the distortion of the nominal sinusoidal caused by a PQ event can be detected.…”

Section: Locally Distributed Node Algorithmmentioning

confidence: 99%

“…With this aim, the input signal is modeled as follows:

where

and

are the in-phase and in-quadrature components of the p -th harmonic, f is the frequency of the signal, P is the number of harmonic components of the signal, and D is the offset. The estimation of the

, and D values can be obtained by using the multi-sine fitting algorithm proposed in [ 19 , 52 ]. The algorithm considers the vector representation of the M samples of the input signal

acquired with a sampling period

, and the variable vector x , which are the harmonics parameters of ( 1 ):

…”

Section: Locally Distributed Node Algorithmmentioning

confidence: 99%

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Local Distributed Node for Power Quality Event Detection Based on Multi-Sine Fitting Algorithm

Carní,

Lamonaca

2024

Sensors

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The new power generation systems, the increasing number of equipment connected to the power grid, and the introduction of technologies such as the smart grid, underline the importance and complexity of the Power Quality (PQ) evaluation. In this scenario, an Automatic PQ Events Classifier (APQEC) that detects, segments, and classifies the anomaly in the power signal is needed for the timely intervention and maintenance of the grid. Due to the extension and complexity of the network, the number of points to be monitored is large, making the cost of the infrastructure unreasonable. To reduce the cost, a new architecture for an APQEC is proposed. This architecture is composed of several Locally Distributed Nodes (LDNs) and a Central Classification Unit (CCU). The LDNs are in charge of the acquisition, the detection of PQ events, and the segmentation of the power signal. Instead, the CCU receives the information from the nodes to classify the PQ events. A low-computational capability characterizes low-cost LDNs. For this reason, a suitable PQ event detection and segmentation method with low resource requirements is proposed. It is based on the use of a sliding observation window that establishes a reasonable time interval, which is also useful for signal classification and the multi-sine fitting algorithm to decompose the input signal in harmonic components. These components can be compared with established threshold values to detect if a PQ event occurs. Only in this case, the signal is sent to the CCU for the classification; otherwise, it is discarded. Numerical tests are performed to set the sliding window size and observe the behavior of the proposed method with the main PQ events presented in the literature, even when the SNR varies. Experimental results confirm the effectiveness of the proposal, highlighting the correspondence with numerical results and the reduced execution time when compared to FFT-based methods.

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Section: Locally Distributed Node Algorithmmentioning

confidence: 99%

“…With this aim, the input signal is modeled as follows:

where

and

are the in-phase and in-quadrature components of the p -th harmonic, f is the frequency of the signal, P is the number of harmonic components of the signal, and D is the offset. The estimation of the

, and D values can be obtained by using the multi-sine fitting algorithm proposed in [ 19 , 52 ]. The algorithm considers the vector representation of the M samples of the input signal

acquired with a sampling period

, and the variable vector x , which are the harmonics parameters of ( 1 ):

…”

Section: Locally Distributed Node Algorithmmentioning

confidence: 99%

Local Distributed Node for Power Quality Event Detection Based on Multi-Sine Fitting Algorithm

Carní,

Lamonaca

2024

Sensors

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show abstract

“…The estimation of sinusoidal signal parameters as amplitude, phase, frequency and offset, is a crucial problem in several application fields [1]- [3]. The main solution proposed in literature are based on the sine fitting algorithms [4]- [8], multi-sine fitting algorithm that is its multi harmonic evolution, [9]- [11], and Discrete Fourier Transform (DFT) [12].The first two solutions have the problems that their accuracy depends on the number of samples and signal period analyzed. Moreover, they require to accurately preliminary evaluate the frequency of the signal, in order to not converge to a local minimum of the solution or to diverge.…”

Section: Introductionmentioning

confidence: 99%

Use of Particle Swarm Optimization Algorithm for Digitalized Sinewave Signal Parameters Estimation

Raimondo¹

2017

IJC

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In the paper is proposed a procedure based on the particle swarm optimization algorithm for parameters estimation of sinewave signals as: amplitude, phase, frequency and offset. Differently from the classical method used to solve this problem (the sine-fitting algorithms), the proposed procedure considers the estimation problem as an optimization one. In fact, the particle swarm algorithm tends to global solution instead of a local solution. The proposed procedure preliminarily estimates the raw value of the parameters under investigation by a time analysis of the input signal. Successively, these values are used by the particle swarm algorithm for the final estimation result. The tests of the proposed procedure determine the most effective cost function for the algorithm and confirm that the achievable performances are in according with the sine fitting algorithm. Moreover, the execution time for the proposed procedure is lower than the sine fitting, making it an interesting alternative.

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Hydromechanical Response and Impact of Gas Mixing Behavior in Subsurface CH₄ Storage with CO₂-Based Cushion Gas

Kempka

et al. 2019

Energy Fuels

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Power-to-gas (PtG) stores chemical energy by converting excess electrical energy from renewable sources into an energy-dense gas. Due to its higher available capacity compared to surface-based storage technologies, subsurface storage in geological systems is the most promising approach for efficient and economic realization of the PtG system's storage component. For this purpose, methane (CH 4 ) produced by methanation by means of hydrogen (H 2 ) and carbon dioxide (CO 2 ) is stored in a geological reservoir until required for further use. In this context, CO 2 is used as the cushion gas to maintain reservoir pressure and limiting working gas, i.e., (CH 4 ) losses during withdrawal periods. Consequently, mixing of both gases in the reservoir is inevitable. Therefore, it is necessary to minimize the gas mixing region to optimize the efficiency of the PtG system's storage component. In the present study, the physical properties of CH 4 , CO 2 and their mixtures are reviewed. Then, a multicomponent flow model is implemented and validated against published data. Next, a hydromechanically coupled model is established, considering fluid flow through porous media and effective stresses to investigate the mixing behavior of both gases and the mechanical reservoir stability. The simulation results show that, with increasing reservoir thickness and dip angle, the mixing region is reduced during gas injection if CO 2 is employed as the cushion gas. In addition, the degree of mixing is lower at higher temperatures. Feasible injection rates and injection schedules can be derived from the integrated reservoir stability analysis. The methodology developed in the present study allows the determination of optimum strategies for storage reservoir selection and gas injection scheduling by minimizing the gas mixing region.

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Static Characterization of Arbitrary Waveform Generator based on Modified Multi- sine Fitting and Zero Crossing Detection Algorithms

Cited by 4 publications

References 16 publications

Local Distributed Node for Power Quality Event Detection Based on Multi-Sine Fitting Algorithm

Local Distributed Node for Power Quality Event Detection Based on Multi-Sine Fitting Algorithm

Use of Particle Swarm Optimization Algorithm for Digitalized Sinewave Signal Parameters Estimation

Hydromechanical Response and Impact of Gas Mixing Behavior in Subsurface CH₄ Storage with CO₂-Based Cushion Gas

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Static Characterization of Arbitrary Waveform Generator based on Modified Multi- sine Fitting and Zero Crossing Detection Algorithms

Cited by 4 publications

References 16 publications

Local Distributed Node for Power Quality Event Detection Based on Multi-Sine Fitting Algorithm

Local Distributed Node for Power Quality Event Detection Based on Multi-Sine Fitting Algorithm

Use of Particle Swarm Optimization Algorithm for Digitalized Sinewave Signal Parameters Estimation

Hydromechanical Response and Impact of Gas Mixing Behavior in Subsurface CH4 Storage with CO2-Based Cushion Gas

Contact Info

Product

Resources

About

Hydromechanical Response and Impact of Gas Mixing Behavior in Subsurface CH₄ Storage with CO₂-Based Cushion Gas