Enhancing efficient solar energy harvesting: A process-in-loop investigation of MPPT control with a novel stochastic algorithm

PV systems currently generate 4% of the world's energy needs, and their share is growing quickly. The maximum power point tracking (MPPT) is a complex non-convex optimization problem because the electrical characteristics of the PV model are nonlinear. Changes in temperature, partial shading (P...

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Main Authors: Muhammad Kamran Khan, Muhammad Hamza Zafar, Talha Riaz, Majad Mansoor, Naureen Akhtar
Format: Article
Language:English
Published: Elsevier 2024-01-01
Series:Energy Conversion and Management: X
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2590174523001654
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author Muhammad Kamran Khan
Muhammad Hamza Zafar
Talha Riaz
Majad Mansoor
Naureen Akhtar
author_facet Muhammad Kamran Khan
Muhammad Hamza Zafar
Talha Riaz
Majad Mansoor
Naureen Akhtar
author_sort Muhammad Kamran Khan
collection DOAJ
description PV systems currently generate 4% of the world's energy needs, and their share is growing quickly. The maximum power point tracking (MPPT) is a complex non-convex optimization problem because the electrical characteristics of the PV model are nonlinear. Changes in temperature, partial shading (PS), and irradiance levels can all affect the amount of power that can be extracted from the solar system. Therefore, in this work, a novel energy valley optimizer (EVO) based MPPT algorithm is suggested to extract maximum power from solar. The classical perturb and observe (P&O), whale optimizer algorithm (WOA), cuckoo search algorithm (CSA), and particle swarm optimization (PSO) algorithms are all compared to EVO. Five case studies, including a field atmospheric data study, partial shading, variable temperature, and irradiance, are used to conduct in-depth analytical and statistical analysis. Furthermore, the successful verification of the MPPT control algorithm on the real microcontroller (Arduino MKRZERO board) through the PIL test is a critical milestone in this research. Quantitative, comparative, statistical and experimental results indicate that the proposed EVO-based MPPT achieves superior performance through 30% quicker tracking time and 80% faster settling time, which result in 4–8% higher power efficiency. The results indicate that the suggested MPPT controller successfully addresses the shortcomings of the current MPPT methods.
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spelling doaj.art-2d9e0a740241495ab01ac2ab8adf8d152023-12-16T06:08:56ZengElsevierEnergy Conversion and Management: X2590-17452024-01-0121100509Enhancing efficient solar energy harvesting: A process-in-loop investigation of MPPT control with a novel stochastic algorithmMuhammad Kamran Khan0Muhammad Hamza Zafar1Talha Riaz2Majad Mansoor3Naureen Akhtar4School of Technology and Innovations, University of Vaasa, FinlandDepartment of Engineering Sciences, University of Agder, NO-4879 Grimstad, NorwayFaculty of Engineering Sciences and Technology, Hamdard University, IslamabadNingbo China Institute for Supply Chain Innovation Ningbo, 315000, ChinaDepartment of Engineering Sciences, University of Agder, NO-4879 Grimstad, Norway; Corresponding author.PV systems currently generate 4% of the world's energy needs, and their share is growing quickly. The maximum power point tracking (MPPT) is a complex non-convex optimization problem because the electrical characteristics of the PV model are nonlinear. Changes in temperature, partial shading (PS), and irradiance levels can all affect the amount of power that can be extracted from the solar system. Therefore, in this work, a novel energy valley optimizer (EVO) based MPPT algorithm is suggested to extract maximum power from solar. The classical perturb and observe (P&O), whale optimizer algorithm (WOA), cuckoo search algorithm (CSA), and particle swarm optimization (PSO) algorithms are all compared to EVO. Five case studies, including a field atmospheric data study, partial shading, variable temperature, and irradiance, are used to conduct in-depth analytical and statistical analysis. Furthermore, the successful verification of the MPPT control algorithm on the real microcontroller (Arduino MKRZERO board) through the PIL test is a critical milestone in this research. Quantitative, comparative, statistical and experimental results indicate that the proposed EVO-based MPPT achieves superior performance through 30% quicker tracking time and 80% faster settling time, which result in 4–8% higher power efficiency. The results indicate that the suggested MPPT controller successfully addresses the shortcomings of the current MPPT methods.http://www.sciencedirect.com/science/article/pii/S2590174523001654Partial shading (PS)Photovoltaic (PV)Energy valley optimizer (EVO)Particle swarm optimization (PSO)Complex partial shading (CPS)Maximum power point tracking (MPPT) controller
spellingShingle Muhammad Kamran Khan
Muhammad Hamza Zafar
Talha Riaz
Majad Mansoor
Naureen Akhtar
Enhancing efficient solar energy harvesting: A process-in-loop investigation of MPPT control with a novel stochastic algorithm
Energy Conversion and Management: X
Partial shading (PS)
Photovoltaic (PV)
Energy valley optimizer (EVO)
Particle swarm optimization (PSO)
Complex partial shading (CPS)
Maximum power point tracking (MPPT) controller
title Enhancing efficient solar energy harvesting: A process-in-loop investigation of MPPT control with a novel stochastic algorithm
title_full Enhancing efficient solar energy harvesting: A process-in-loop investigation of MPPT control with a novel stochastic algorithm
title_fullStr Enhancing efficient solar energy harvesting: A process-in-loop investigation of MPPT control with a novel stochastic algorithm
title_full_unstemmed Enhancing efficient solar energy harvesting: A process-in-loop investigation of MPPT control with a novel stochastic algorithm
title_short Enhancing efficient solar energy harvesting: A process-in-loop investigation of MPPT control with a novel stochastic algorithm
title_sort enhancing efficient solar energy harvesting a process in loop investigation of mppt control with a novel stochastic algorithm
topic Partial shading (PS)
Photovoltaic (PV)
Energy valley optimizer (EVO)
Particle swarm optimization (PSO)
Complex partial shading (CPS)
Maximum power point tracking (MPPT) controller
url http://www.sciencedirect.com/science/article/pii/S2590174523001654
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