A Novel Approach to Achieve MPPT for Photovoltaic System Based SCADA

Bilal Naji Alhasnawi (), Basil H. Jasim, Arshad Naji Alhasnawi, Bishoy E. Sedhom, Ali M. Jasim, Azam Khalili, Vladimír Bureš, Alessandro Burgio and Pierluigi Siano ()
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Bilal Naji Alhasnawi: Department of Computer Technical Engineering, College of Information Technology, Imam Ja’afar Al-Sadiq University, Al-Muthanna 66002, Iraq
Basil H. Jasim: Electrical Engineering Department, Basrah University, Basrah 61001, Iraq
Arshad Naji Alhasnawi: Department of Biology, College of Education for Pure Sciences, Al-Muthanna University, Samawah 66001, Iraq
Bishoy E. Sedhom: Electrical Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
Ali M. Jasim: Electrical Engineering Department, Basrah University, Basrah 61001, Iraq
Azam Khalili: Department of Electrical Engineering, Malayer University, Malayer 65719-95863, Iran
Vladimír Bureš: Faculty of Informatics and Management, University of Hradec Králové, 50003 Hradec Králové, Czech Republic
Alessandro Burgio: Independent Researcher, 20124 Milano, Italy
Pierluigi Siano: Management and Innovation Systems Department, Salerno University, 84084 Fisciano, Italy

Energies, 2022, vol. 15, issue 22, 1-29

Abstract: 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.

Keywords: MPPT; SCADA; solar system; Internet of Things (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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