An Intelligent Classification Method of Series Arc Fault Models Using Deep Learning Algorithm

Omran, Alaa Hamza; Said, Dalila Mat; Hussin, Siti Maherah; Mirsaeidi, Sohrab; Abid, Yaser M.

doi:10.1109/pecon48942.2020.9314520

Cited by 10 publications

(3 citation statements)

References 25 publications

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“…It accomplishes this by employing a data mining strategy to process the functions. A semi-definite embedding (SDE) is a method used to perform a nonlinear dimensional reduction of a vector information source [15][16][17], and [18]. is method is utilised to reveal the data's maximum dimensions.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Environmental and Geographical (EG) Image Classification Using FLIM and CNN Algorithms

Ajay

Nagaraj

Huang

et al. 2022

Contrast Media & Molecular Imaging

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Intelligent machines have grown in importance in recent years in object recognition in terms of their ability to envision, comprehend, and reach decisions. There are a lot of complicated algorithms that accomplish AI utilities. In addition to their use in the medical industry, these methods of object recognition have a wide range of other fields, most notably industries, in which they can be applied. In contrast to the proposed calculation, the proposed calculation is less complex and more accurate under certain SNR conditions. In the deep nervous tissue fine-tuning discriminator, phantom highlights and binding highlights are separated as sources; modified direct components are used as neuronal activation abilities; and cross entropy is used as unfortunate abilities. Optimized recognition of profound nervous tissue builds profound and periodic nervous tissue for regulatory confirmation of the corresponding signal.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Environmental and Geographical (EG) Image Classification Using FLIM and CNN Algorithms

Ajay

Nagaraj

Huang

et al. 2022

Contrast Media & Molecular Imaging

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“…Nine different models of arc fault are simulated, where each model have its own several constants that allow it to simulate different cases. These models associated with their parameters are presented as follows [17]- [19]:…”

Section: Series Arc Modelsmentioning

confidence: 99%

Photovoltaic system DC series arc fault: a case study

Omran

Said

Hussin

et al. 2022

IJEECS

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<p>Photovoltaic (PV) systems are becoming increasingly popular; however, arc faults on the direct current (DC) side are becoming more widespread as a result of the effects of aging as well as the trend toward higher DC voltage levels, posing severe risk to human safety and system stability. The parallel arc faults present higher level of current as compared with the series arc faults, making it more difficult to spot the series arc. In this paper and for the aim of condition monitoring, the features of a DC series arc fault are analyzed by analysing the arc features, performing model’s simulation in PSCAD, and carrying out experimental studies. Various arc models are simulated and investigated; for low current arcs, the heuristic model is used where a set of parameters established. Moreover, the heuristic model’s simulated arc has been shown to be compatible with the experimental data. The features of arc noise in the electrode separation region and steady-arcing states with varied gap widths are investigated. It has been discovered that after an arc fault occurs, arc noise increases, notably in the frequency range below 50 kHz; where this property is useful for detecting DC series arc faults. Besides that, variations in air gap width are more sensitive to frequencies under 5 kHz.</p>

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“…In [10], a semisupervised machine learning approach is used to handle both labelled and unlabeled data by co-training DTs and k-NN classifiers to classify faults in both transmission and distribution systems including MGs. In [15], the series arc faults in PV systems are classified by generating different types of series arc faults by training the corresponding data with the light convolution neural network. is approach heavily relied on expert-designed characteristics from the simulations and is identified to be difficult while training models with strong generalization.…”

Section: Introductionmentioning

confidence: 99%

Fault Classification with Convolutional Neural Networks for Microgrid Systems

Pan

Mandal

Akanda

2022

International Transactions on Electrical Energy Systems

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The microgrid (MG) networks require adaptive and rapid fault classification mechanisms due to their insufficient kinetic energy reserve and dynamic response of power electronic converters of distributed generation (DG) systems. To achieve this requirement, this study explores the issues in standalone (SA) and grid-connected (GC) operating modes of MG and develops a near-real-time intelligent disturbance detection and protective solutions for their stable operation. In the proposed approach, an intelligent fault classification mechanism is developed using the advantages of wavelet transform and convolutional neural networks (CNNs). Initially, the voltage and current outcomes for each and every possible fault in the MG network are identified and the wavelet transforms are applied for preprocessing and image conversion. The converted images are identified as scalograms which are further trained with the CNNs. To assess the development of the proposed approach, the IEEE 13 bus system is considered for data gathering. To replicate the real-time behavior of the MG network, the additive white Gaussian noise (AWGN) and additive impulsive Gaussian noise (AIGN) are injected at various levels during the classifier development process. The trained classifier has an average training accuracy of 99.1% for SA MG and 97.7% for GC MG, and the average testing accuracies are 98.9% for SA MG and 97.1% for GC MG.

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An Intelligent Classification Method of Series Arc Fault Models Using Deep Learning Algorithm

Cited by 10 publications

References 25 publications

[Retracted] Environmental and Geographical (EG) Image Classification Using FLIM and CNN Algorithms

[Retracted] Environmental and Geographical (EG) Image Classification Using FLIM and CNN Algorithms

Photovoltaic system DC series arc fault: a case study

Fault Classification with Convolutional Neural Networks for Microgrid Systems

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