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Control energy management system for photovoltaic with bidirectional converter using deep neural network

Rapid population growth propels technological advancement, heightening electricity demand. Obsolete fossil fuel-based power facilities necessitate alternative energy sources. Photovoltaic (PV) energy relies on weather conditions, posing challenges for constant energy consumption. This hybrid energy...

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主要な著者: Widjonarko, Widjonarko, Wahyu Mulyo Utomo, Wahyu Mulyo Utomo, Omar, Saodah, Fatah Ridha Baskara, Fatah Ridha Baskara, Rosyadi, Marwan
フォーマット: 論文
言語:English
出版事項: Ijere 2024
主題:
T Technology (General)
オンライン･アクセス:http://eprints.uthm.edu.my/11086/1/J17573_1e25673a7bb22e7dd28b1b0d45b81592.pdf
http://eprints.uthm.edu.my/11086/
https://doi.org/10.11591/ijece.v14i2.pp1437-1447
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id my.uthm.eprints.11086
record_format eprints
spelling my.uthm.eprints.110862024-06-04T03:05:16Z http://eprints.uthm.edu.my/11086/ Control energy management system for photovoltaic with bidirectional converter using deep neural network Widjonarko, Widjonarko Wahyu Mulyo Utomo, Wahyu Mulyo Utomo Omar, Saodah Fatah Ridha Baskara, Fatah Ridha Baskara Rosyadi, Marwan T Technology (General) Rapid population growth propels technological advancement, heightening electricity demand. Obsolete fossil fuel-based power facilities necessitate alternative energy sources. Photovoltaic (PV) energy relies on weather conditions, posing challenges for constant energy consumption. This hybrid energy source system (HESS) prototype employs extreme learning machine (ELM) power management to oversee PV, fossil fuel, and battery sources. ELM optimally selects power sources, adapting to varying conditions. A bidirectional converter (BDC) efficiently manages battery charging, discharging, and secondary power distribution. HESS ensures continuous load supply and swift response for system reliability. The optimal HESS design incorporates a single renewable source (PV), conventional energy (PNL and genset), and energy storage (battery). Supported by a BDC with over 80% efficiency in buck and boost modes, it stabilizes voltage and supplies power through flawless ELM-free logic verification. Google Colab online testing and hardware implementation with Arduino demonstrate ELM's reliability, maintaining a direct current (DC) 24 V interface voltage and ensuring its applicability for optimal HESS. Ijere 2024 Article PeerReviewed text en http://eprints.uthm.edu.my/11086/1/J17573_1e25673a7bb22e7dd28b1b0d45b81592.pdf Widjonarko, Widjonarko and Wahyu Mulyo Utomo, Wahyu Mulyo Utomo and Omar, Saodah and Fatah Ridha Baskara, Fatah Ridha Baskara and Rosyadi, Marwan (2024) Control energy management system for photovoltaic with bidirectional converter using deep neural network. International Journal of Electrical and Computer Engineering (IJECE), 14 (2). pp. 1437-1447. ISSN 2088-8708 https://doi.org/10.11591/ijece.v14i2.pp1437-1447
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Widjonarko, Widjonarko
Wahyu Mulyo Utomo, Wahyu Mulyo Utomo
Omar, Saodah
Fatah Ridha Baskara, Fatah Ridha Baskara
Rosyadi, Marwan
Control energy management system for photovoltaic with bidirectional converter using deep neural network
description Rapid population growth propels technological advancement, heightening electricity demand. Obsolete fossil fuel-based power facilities necessitate alternative energy sources. Photovoltaic (PV) energy relies on weather conditions, posing challenges for constant energy consumption. This hybrid energy source system (HESS) prototype employs extreme learning machine (ELM) power management to oversee PV, fossil fuel, and battery sources. ELM optimally selects power sources, adapting to varying conditions. A bidirectional converter (BDC) efficiently manages battery charging, discharging, and secondary power distribution. HESS ensures continuous load supply and swift response for system reliability. The optimal HESS design incorporates a single renewable source (PV), conventional energy (PNL and genset), and energy storage (battery). Supported by a BDC with over 80% efficiency in buck and boost modes, it stabilizes voltage and supplies power through flawless ELM-free logic verification. Google Colab online testing and hardware implementation with Arduino demonstrate ELM's reliability, maintaining a direct current (DC) 24 V interface voltage and ensuring its applicability for optimal HESS.
format Article
author Widjonarko, Widjonarko
Wahyu Mulyo Utomo, Wahyu Mulyo Utomo
Omar, Saodah
Fatah Ridha Baskara, Fatah Ridha Baskara
Rosyadi, Marwan
author_facet Widjonarko, Widjonarko
Wahyu Mulyo Utomo, Wahyu Mulyo Utomo
Omar, Saodah
Fatah Ridha Baskara, Fatah Ridha Baskara
Rosyadi, Marwan
author_sort Widjonarko, Widjonarko
title Control energy management system for photovoltaic with bidirectional converter using deep neural network
title_short Control energy management system for photovoltaic with bidirectional converter using deep neural network
title_full Control energy management system for photovoltaic with bidirectional converter using deep neural network
title_fullStr Control energy management system for photovoltaic with bidirectional converter using deep neural network
title_full_unstemmed Control energy management system for photovoltaic with bidirectional converter using deep neural network
title_sort control energy management system for photovoltaic with bidirectional converter using deep neural network
publisher Ijere
publishDate 2024
url http://eprints.uthm.edu.my/11086/1/J17573_1e25673a7bb22e7dd28b1b0d45b81592.pdf
http://eprints.uthm.edu.my/11086/
https://doi.org/10.11591/ijece.v14i2.pp1437-1447
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score 13.251813

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