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
Gespeichert in:
| Autor*in: |
Alaa Hamza Omran [verfasserIn] Dalila Mat Said [verfasserIn] Siti Maherah Hussin [verfasserIn] Sadiq H. Abdulhussein [verfasserIn] Nasarudin Ahmad [verfasserIn] Haidar Samet [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2023 |
|---|
| Schlagwörter: |
|---|
| Übergeordnetes Werk: |
In: Iraqi Journal for Computer Science and Mathematics - College of Education, Al-Iraqia University, 2022, 4(2023), 3 |
|---|---|
| Übergeordnetes Werk: |
volume:4 ; year:2023 ; number:3 |
| Links: |
Link aufrufen |
|---|
| DOI / URN: |
10.52866/ijcsm.2023.02.03.014 |
|---|
| Katalog-ID: |
DOAJ096807202 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | DOAJ096807202 | ||
| 003 | DE-627 | ||
| 005 | 20240413162358.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240413s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.52866/ijcsm.2023.02.03.014 |2 doi | |
| 035 | |a (DE-627)DOAJ096807202 | ||
| 035 | |a (DE-599)DOAJ4af8bd1eb87f47b6aae40ccec54f17e0 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 050 | 0 | |a QA75.5-76.95 | |
| 100 | 0 | |a Alaa Hamza Omran |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Development of an Intelligent Detection Method of DC Series Arc Fault in Photovoltaic System Using Multilayer Perceptron and Bi-Directional Long Short-Term Memory |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a 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. | ||
| 650 | 4 | |a DC Fault | |
| 650 | 4 | |a Series Arc Fault | |
| 650 | 4 | |a Bi-LSTM | |
| 650 | 4 | |a CNN | |
| 653 | 0 | |a Electronic computers. Computer science | |
| 700 | 0 | |a Dalila Mat Said |e verfasserin |4 aut | |
| 700 | 0 | |a Siti Maherah Hussin |e verfasserin |4 aut | |
| 700 | 0 | |a Sadiq H. Abdulhussein |e verfasserin |4 aut | |
| 700 | 0 | |a Nasarudin Ahmad |e verfasserin |4 aut | |
| 700 | 0 | |a Haidar Samet |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i In |t Iraqi Journal for Computer Science and Mathematics |d College of Education, Al-Iraqia University, 2022 |g 4(2023), 3 |w (DE-627)DOAJ000150312 |x 27887421 |7 nnns |
| 773 | 1 | 8 | |g volume:4 |g year:2023 |g number:3 |
| 856 | 4 | 0 | |u https://doi.org/10.52866/ijcsm.2023.02.03.014 |z kostenfrei |
| 856 | 4 | 0 | |u https://doaj.org/article/4af8bd1eb87f47b6aae40ccec54f17e0 |z kostenfrei |
| 856 | 4 | 0 | |u https://journal.esj.edu.iq/index.php/IJCM/article/view/986 |z kostenfrei |
| 856 | 4 | 2 | |u https://doaj.org/toc/2958-0544 |y Journal toc |z kostenfrei |
| 856 | 4 | 2 | |u https://doaj.org/toc/2788-7421 |y Journal toc |z kostenfrei |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_DOAJ | ||
| 951 | |a AR | ||
| 952 | |d 4 |j 2023 |e 3 | ||
| author_variant |
a h o aho d m s dms s m h smh s h a sha n a na h s hs |
|---|---|
| matchkey_str |
article:27887421:2023----::eeomnoaitlieteetomtoodsreacalipoootisseuigutlyrecpr |
| hierarchy_sort_str |
2023 |
| callnumber-subject-code |
QA |
| publishDate |
2023 |
| allfields |
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 |
| spelling |
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 |
| allfields_unstemmed |
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 |
| allfieldsGer |
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 |
| allfieldsSound |
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 |
| language |
English |
| source |
In Iraqi Journal for Computer Science and Mathematics 4(2023), 3 volume:4 year:2023 number:3 |
| sourceStr |
In Iraqi Journal for Computer Science and Mathematics 4(2023), 3 volume:4 year:2023 number:3 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
DC Fault Series Arc Fault Bi-LSTM CNN Electronic computers. Computer science |
| isfreeaccess_bool |
true |
| container_title |
Iraqi Journal for Computer Science and Mathematics |
| authorswithroles_txt_mv |
Alaa Hamza Omran @@aut@@ Dalila Mat Said @@aut@@ Siti Maherah Hussin @@aut@@ Sadiq H. Abdulhussein @@aut@@ Nasarudin Ahmad @@aut@@ Haidar Samet @@aut@@ |
| publishDateDaySort_date |
2023-01-01T00:00:00Z |
| hierarchy_top_id |
DOAJ000150312 |
| id |
DOAJ096807202 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">DOAJ096807202</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240413162358.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240413s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.52866/ijcsm.2023.02.03.014</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ096807202</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ4af8bd1eb87f47b6aae40ccec54f17e0</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QA75.5-76.95</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Alaa Hamza Omran</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Development of an Intelligent Detection Method of DC Series Arc Fault in Photovoltaic System Using Multilayer Perceptron and Bi-Directional Long Short-Term Memory</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">DC Fault</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Series Arc Fault</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Bi-LSTM</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">CNN</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Electronic computers. Computer science</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Dalila Mat Said</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Siti Maherah Hussin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Sadiq H. Abdulhussein</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Nasarudin Ahmad</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Haidar Samet</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Iraqi Journal for Computer Science and Mathematics</subfield><subfield code="d">College of Education, Al-Iraqia University, 2022</subfield><subfield code="g">4(2023), 3</subfield><subfield code="w">(DE-627)DOAJ000150312</subfield><subfield code="x">27887421</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:4</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:3</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.52866/ijcsm.2023.02.03.014</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/4af8bd1eb87f47b6aae40ccec54f17e0</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://journal.esj.edu.iq/index.php/IJCM/article/view/986</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2958-0544</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2788-7421</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">4</subfield><subfield code="j">2023</subfield><subfield code="e">3</subfield></datafield></record></collection>
|
| callnumber-first |
Q - Science |
| author |
Alaa Hamza Omran |
| spellingShingle |
Alaa Hamza Omran misc QA75.5-76.95 misc DC Fault misc Series Arc Fault misc Bi-LSTM misc CNN misc Electronic computers. Computer science Development of an Intelligent Detection Method of DC Series Arc Fault in Photovoltaic System Using Multilayer Perceptron and Bi-Directional Long Short-Term Memory |
| authorStr |
Alaa Hamza Omran |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)DOAJ000150312 |
| format |
electronic Article |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut aut aut |
| collection |
DOAJ |
| remote_str |
true |
| callnumber-label |
QA75 |
| illustrated |
Not Illustrated |
| issn |
27887421 |
| topic_title |
QA75.5-76.95 Development of an Intelligent Detection Method of DC Series Arc Fault in Photovoltaic System Using Multilayer Perceptron and Bi-Directional Long Short-Term Memory DC Fault Series Arc Fault Bi-LSTM CNN |
| topic |
misc QA75.5-76.95 misc DC Fault misc Series Arc Fault misc Bi-LSTM misc CNN misc Electronic computers. Computer science |
| topic_unstemmed |
misc QA75.5-76.95 misc DC Fault misc Series Arc Fault misc Bi-LSTM misc CNN misc Electronic computers. Computer science |
| topic_browse |
misc QA75.5-76.95 misc DC Fault misc Series Arc Fault misc Bi-LSTM misc CNN misc Electronic computers. Computer science |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
cr |
| hierarchy_parent_title |
Iraqi Journal for Computer Science and Mathematics |
| hierarchy_parent_id |
DOAJ000150312 |
| hierarchy_top_title |
Iraqi Journal for Computer Science and Mathematics |
| isfreeaccess_txt |
true |
| familylinks_str_mv |
(DE-627)DOAJ000150312 |
| title |
Development of an Intelligent Detection Method of DC Series Arc Fault in Photovoltaic System Using Multilayer Perceptron and Bi-Directional Long Short-Term Memory |
| ctrlnum |
(DE-627)DOAJ096807202 (DE-599)DOAJ4af8bd1eb87f47b6aae40ccec54f17e0 |
| title_full |
Development of an Intelligent Detection Method of DC Series Arc Fault in Photovoltaic System Using Multilayer Perceptron and Bi-Directional Long Short-Term Memory |
| author_sort |
Alaa Hamza Omran |
| journal |
Iraqi Journal for Computer Science and Mathematics |
| journalStr |
Iraqi Journal for Computer Science and Mathematics |
| callnumber-first-code |
Q |
| lang_code |
eng |
| isOA_bool |
true |
| recordtype |
marc |
| publishDateSort |
2023 |
| contenttype_str_mv |
txt |
| author_browse |
Alaa Hamza Omran Dalila Mat Said Siti Maherah Hussin Sadiq H. Abdulhussein Nasarudin Ahmad Haidar Samet |
| container_volume |
4 |
| class |
QA75.5-76.95 |
| format_se |
Elektronische Aufsätze |
| author-letter |
Alaa Hamza Omran |
| doi_str_mv |
10.52866/ijcsm.2023.02.03.014 |
| author2-role |
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 |
| callnumber |
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. |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ |
| container_issue |
3 |
| 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 |
| remote_bool |
true |
| 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 |
| ppnlink |
DOAJ000150312 |
| callnumber-subject |
QA - Mathematics |
| mediatype_str_mv |
c |
| isOA_txt |
true |
| hochschulschrift_bool |
false |
| doi_str |
10.52866/ijcsm.2023.02.03.014 |
| callnumber-a |
QA75.5-76.95 |
| up_date |
2024-07-03T22:24:11.663Z |
| _version_ |
1803598379524030464 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">DOAJ096807202</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240413162358.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240413s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.52866/ijcsm.2023.02.03.014</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ096807202</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ4af8bd1eb87f47b6aae40ccec54f17e0</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QA75.5-76.95</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Alaa Hamza Omran</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Development of an Intelligent Detection Method of DC Series Arc Fault in Photovoltaic System Using Multilayer Perceptron and Bi-Directional Long Short-Term Memory</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">DC Fault</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Series Arc Fault</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Bi-LSTM</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">CNN</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Electronic computers. Computer science</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Dalila Mat Said</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Siti Maherah Hussin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Sadiq H. Abdulhussein</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Nasarudin Ahmad</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Haidar Samet</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Iraqi Journal for Computer Science and Mathematics</subfield><subfield code="d">College of Education, Al-Iraqia University, 2022</subfield><subfield code="g">4(2023), 3</subfield><subfield code="w">(DE-627)DOAJ000150312</subfield><subfield code="x">27887421</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:4</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:3</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.52866/ijcsm.2023.02.03.014</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/4af8bd1eb87f47b6aae40ccec54f17e0</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://journal.esj.edu.iq/index.php/IJCM/article/view/986</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2958-0544</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2788-7421</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">4</subfield><subfield code="j">2023</subfield><subfield code="e">3</subfield></datafield></record></collection>
|
| score |
7.4005327 |