Статья

A Novel Approach to Achieve MPPT for Photovoltaic System Based SCADA

Bilal Naji AlhasnawiDepartment of Computer Technical Engineering, College of Information Technology, Imam Ja’afar Al-Sadiq University, Al-Muthanna 66002, IraqBasil H. JasimElectrical Engineering Department, Basrah University, Basrah 61001, IraqArshad Naji AlhasnawiDepartment of Biology, College of Education for Pure Sciences, Al-Muthanna University, Samawah 66001, IraqBishoy E. SedhomElectrical Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35516, EgyptAli M. JasimDepartment of Communications Engineering, Iraq University College, Basrah 61001, IraqAzam KhaliliDepartment of Electrical Engineering, Malayer University, Malayer 65719-95863, IranVladimír BurešFaculty of Informatics and Management, University of Hradec Králové, 50003 Hradec Králové, Czech RepublicAlessandro BurgioPierluigi SianoDepartment of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa

2022en

ABI

Аннотация

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.

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Идентификаторы

DOI: 10.3390/en15228480

Цитирования и источники

Цитирований: 2Использованных источников: 0

Показатели — AkademScholar