A Novel Approach to Achieve MPPT for Photovoltaic System Based SCADA
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F22%3A50020233" target="_blank" >RIV/62690094:18450/22:50020233 - isvavai.cz</a>
Výsledek na webu
<a href="https://www.mdpi.com/1996-1073/15/22/8480" target="_blank" >https://www.mdpi.com/1996-1073/15/22/8480</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/en15228480" target="_blank" >10.3390/en15228480</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
A Novel Approach to Achieve MPPT for Photovoltaic System Based SCADA
Popis výsledku v původním jazyce
In this study, an improved artificial intelligence algorithms augmented Internet of Things (IoT)-based maximum power point tracking (MPPT) for photovoltaic (PV) system has been proposed. This will facilitate preventive maintenance, fault detection, and historical analysis of the plant in addition to real-time monitoring. Further, the simulation results validate the improved performance of the suggested method. To demonstrate the superiority of the proposed MPPT algorithm over current methods, such as cuckoo search algorithms and the incremental conductance approach, a performance comparison is offered. The outcomes demonstrate the suggested algorithm's capability to track the Global Maximum Power Point (GMPP) with quicker convergence and less power oscillations than before. The results clearly show that the artificial intelligence algorithm-based MPPT is capable of tracking the GMPP with an average efficiency of 88%, and an average tracking time of 0.029 s, proving both its viability and effectiveness.
Název v anglickém jazyce
A Novel Approach to Achieve MPPT for Photovoltaic System Based SCADA
Popis výsledku anglicky
In this study, an improved artificial intelligence algorithms augmented Internet of Things (IoT)-based maximum power point tracking (MPPT) for photovoltaic (PV) system has been proposed. This will facilitate preventive maintenance, fault detection, and historical analysis of the plant in addition to real-time monitoring. Further, the simulation results validate the improved performance of the suggested method. To demonstrate the superiority of the proposed MPPT algorithm over current methods, such as cuckoo search algorithms and the incremental conductance approach, a performance comparison is offered. The outcomes demonstrate the suggested algorithm's capability to track the Global Maximum Power Point (GMPP) with quicker convergence and less power oscillations than before. The results clearly show that the artificial intelligence algorithm-based MPPT is capable of tracking the GMPP with an average efficiency of 88%, and an average tracking time of 0.029 s, proving both its viability and effectiveness.
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
20704 - Energy and fuels
Návaznosti výsledku
Projekt
—
Návaznosti
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Ostatní
Rok uplatnění
2022
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
ENERGIES
ISSN
1996-1073
e-ISSN
1996-1073
Svazek periodika
15
Číslo periodika v rámci svazku
22
Stát vydavatele periodika
CH - Švýcarská konfederace
Počet stran výsledku
29
Strana od-do
"Article Number: 8480"
Kód UT WoS článku
000887191900001
EID výsledku v databázi Scopus
2-s2.0-85142679245