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

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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書目詳細資料
發表在:International Journal of Electrical and Computer Engineering
主要作者: 2-s2.0-85185810947
格式: Article
語言:English
出版: Institute of Advanced Engineering and Science 2024
在線閱讀:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185810947&doi=10.11591%2fijece.v14i2.pp1437-1447&partnerID=40&md5=fe504505c0d4c97a54b70c65b8488757
id Widjonarko; Utomo W.M.; Omar S.; Baskara F.R.; Rosyadi M.
spelling Widjonarko; Utomo W.M.; Omar S.; Baskara F.R.; Rosyadi M.
2-s2.0-85185810947
Control energy management system for photovoltaic with bidirectional converter using deep neural network
2024
International Journal of Electrical and Computer Engineering
14
2
10.11591/ijece.v14i2.pp1437-1447
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185810947&doi=10.11591%2fijece.v14i2.pp1437-1447&partnerID=40&md5=fe504505c0d4c97a54b70c65b8488757
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. © 2024 Institute of Advanced Engineering and Science. All rights reserved.
Institute of Advanced Engineering and Science
20888708
English
Article
All Open Access; Gold Open Access
author 2-s2.0-85185810947
spellingShingle 2-s2.0-85185810947
Control energy management system for photovoltaic with bidirectional converter using deep neural network
author_facet 2-s2.0-85185810947
author_sort 2-s2.0-85185810947
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
publishDate 2024
container_title International Journal of Electrical and Computer Engineering
container_volume 14
container_issue 2
doi_str_mv 10.11591/ijece.v14i2.pp1437-1447
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185810947&doi=10.11591%2fijece.v14i2.pp1437-1447&partnerID=40&md5=fe504505c0d4c97a54b70c65b8488757
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. © 2024 Institute of Advanced Engineering and Science. All rights reserved.
publisher Institute of Advanced Engineering and Science
issn 20888708
language English
format Article
accesstype All Open Access; Gold Open Access
record_format scopus
collection Scopus
_version_ 1828987863096623104

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