Development of an Intelligent Detection Method of DC Series Arc Fault in Photovoltaic System Using Multilayer Perceptron and Bi-Directional Long Short-Term Memory
A DC series arc fault is one of the significant sources of electrical wiring fires in residential buildings. The production of extremely high temperatures may lead to the ignition of nearby combustible materials. The applications of arc fault diagnosis based machine learning are a global interest du...
Ausführliche Beschreibung
Autor*in: |
Alaa Hamza Omran [verfasserIn] Dalila Mat Said [verfasserIn] Siti Maherah Hussin [verfasserIn] Sadiq H. Abdulhussein [verfasserIn] Nasarudin Ahmad [verfasserIn] Haidar Samet [verfasserIn] |
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Sprache: |
Englisch |
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2023 |
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In: Iraqi Journal for Computer Science and Mathematics - College of Education, Al-Iraqia University, 2022, 4(2023), 3 |
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Übergeordnetes Werk: |
volume:4 ; year:2023 ; number:3 |
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Link aufrufen |
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DOI / URN: |
10.52866/ijcsm.2023.02.03.014 |
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DOAJ096807202 |
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10.52866/ijcsm.2023.02.03.014 doi (DE-627)DOAJ096807202 (DE-599)DOAJ4af8bd1eb87f47b6aae40ccec54f17e0 DE-627 ger DE-627 rakwb eng QA75.5-76.95 Alaa Hamza Omran verfasserin aut Development of an Intelligent Detection Method of DC Series Arc Fault in Photovoltaic System Using Multilayer Perceptron and Bi-Directional Long Short-Term Memory 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A DC series arc fault is one of the significant sources of electrical wiring fires in residential buildings. The production of extremely high temperatures may lead to the ignition of nearby combustible materials. The applications of arc fault diagnosis based machine learning are a global interest due to the immense challenge to create an accurate and efficient detection method. In this paper, a detection and classification method using a multilayer perceptron incorporated with Bi-Directional Long shortterm Memory (MLP-BiLSTM) is proposed. In order to achieve this goal, nine series arc fault models are used in conjunction with data from real-world observations for simulation purposes using Power System Computer Aided Design (PSCAD) software. The simulation and experimental results confirm that the accuracy of the proposed detection and classification method reaches 99%, which results in that the methodology is believed to be accurate for DC series arc fault detection and classification in the PV system with relatively high accuracy. DC Fault Series Arc Fault Bi-LSTM CNN Electronic computers. Computer science Dalila Mat Said verfasserin aut Siti Maherah Hussin verfasserin aut Sadiq H. Abdulhussein verfasserin aut Nasarudin Ahmad verfasserin aut Haidar Samet verfasserin aut In Iraqi Journal for Computer Science and Mathematics College of Education, Al-Iraqia University, 2022 4(2023), 3 (DE-627)DOAJ000150312 27887421 nnns volume:4 year:2023 number:3 https://doi.org/10.52866/ijcsm.2023.02.03.014 kostenfrei https://doaj.org/article/4af8bd1eb87f47b6aae40ccec54f17e0 kostenfrei https://journal.esj.edu.iq/index.php/IJCM/article/view/986 kostenfrei https://doaj.org/toc/2958-0544 Journal toc kostenfrei https://doaj.org/toc/2788-7421 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 4 2023 3 |
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verfasserin |
title_sort |
development of an intelligent detection method of dc series arc fault in photovoltaic system using multilayer perceptron and bi-directional long short-term memory |
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QA75.5-76.95 |
title_auth |
Development of an Intelligent Detection Method of DC Series Arc Fault in Photovoltaic System Using Multilayer Perceptron and Bi-Directional Long Short-Term Memory |
abstract |
A DC series arc fault is one of the significant sources of electrical wiring fires in residential buildings. The production of extremely high temperatures may lead to the ignition of nearby combustible materials. The applications of arc fault diagnosis based machine learning are a global interest due to the immense challenge to create an accurate and efficient detection method. In this paper, a detection and classification method using a multilayer perceptron incorporated with Bi-Directional Long shortterm Memory (MLP-BiLSTM) is proposed. In order to achieve this goal, nine series arc fault models are used in conjunction with data from real-world observations for simulation purposes using Power System Computer Aided Design (PSCAD) software. The simulation and experimental results confirm that the accuracy of the proposed detection and classification method reaches 99%, which results in that the methodology is believed to be accurate for DC series arc fault detection and classification in the PV system with relatively high accuracy. |
abstractGer |
A DC series arc fault is one of the significant sources of electrical wiring fires in residential buildings. The production of extremely high temperatures may lead to the ignition of nearby combustible materials. The applications of arc fault diagnosis based machine learning are a global interest due to the immense challenge to create an accurate and efficient detection method. In this paper, a detection and classification method using a multilayer perceptron incorporated with Bi-Directional Long shortterm Memory (MLP-BiLSTM) is proposed. In order to achieve this goal, nine series arc fault models are used in conjunction with data from real-world observations for simulation purposes using Power System Computer Aided Design (PSCAD) software. The simulation and experimental results confirm that the accuracy of the proposed detection and classification method reaches 99%, which results in that the methodology is believed to be accurate for DC series arc fault detection and classification in the PV system with relatively high accuracy. |
abstract_unstemmed |
A DC series arc fault is one of the significant sources of electrical wiring fires in residential buildings. The production of extremely high temperatures may lead to the ignition of nearby combustible materials. The applications of arc fault diagnosis based machine learning are a global interest due to the immense challenge to create an accurate and efficient detection method. In this paper, a detection and classification method using a multilayer perceptron incorporated with Bi-Directional Long shortterm Memory (MLP-BiLSTM) is proposed. In order to achieve this goal, nine series arc fault models are used in conjunction with data from real-world observations for simulation purposes using Power System Computer Aided Design (PSCAD) software. The simulation and experimental results confirm that the accuracy of the proposed detection and classification method reaches 99%, which results in that the methodology is believed to be accurate for DC series arc fault detection and classification in the PV system with relatively high accuracy. |
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title_short |
Development of an Intelligent Detection Method of DC Series Arc Fault in Photovoltaic System Using Multilayer Perceptron and Bi-Directional Long Short-Term Memory |
url |
https://doi.org/10.52866/ijcsm.2023.02.03.014 https://doaj.org/article/4af8bd1eb87f47b6aae40ccec54f17e0 https://journal.esj.edu.iq/index.php/IJCM/article/view/986 https://doaj.org/toc/2958-0544 https://doaj.org/toc/2788-7421 |
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author2 |
Dalila Mat Said Siti Maherah Hussin Sadiq H. Abdulhussein Nasarudin Ahmad Haidar Samet |
author2Str |
Dalila Mat Said Siti Maherah Hussin Sadiq H. Abdulhussein Nasarudin Ahmad Haidar Samet |
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QA - Mathematics |
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doi_str |
10.52866/ijcsm.2023.02.03.014 |
callnumber-a |
QA75.5-76.95 |
up_date |
2024-07-03T22:24:11.663Z |
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