Real-time implementation of a novel MPPT control based on the improved PSO algorithm using an adaptive factor selection strategy for photovoltaic systems

Ben Regaya, Chiheb; Hamdi, Hichem; Farhani, Fethi; Marai, Afef; Zaafouri, Abderrahmen; Chaari, Abdelkader

doi:10.1016/j.isatra.2023.12.024

Our system is currently under heavy load due to increased usage. We're actively working on upgrades to improve performance. Thank you for your patience.

ISA Transactions

2024

DOI: 10.1016/j.isatra.2023.12.024

|View full text |Cite

Real-time implementation of a novel MPPT control based on the improved PSO algorithm using an adaptive factor selection strategy for photovoltaic systems

Chiheb Ben Regaya,

Hichem Hamdi,

Fethi Farhani

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article5

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 14 publications

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Conventional and artificial intelligence based maximum power point tracking techniques for efficient solar power generation

Khan,

Raza,

Faheem

et al. 2024

Engineering Reports

View full text Add to dashboard Cite

The increasing global need for renewable energy sources, driven by environmental concerns and the limited availability of traditional energy, highlights the significance of solar energy. However, weather fluctuations challenge the efficiency of solar systems, making maximum power point tracking (MPPT) systems crucial for optimal energy harvesting. This study compares ten MPPT approaches, including both conventional and artificial intelligence (AI)‐based techniques. These controllers were designed and implemented using MATLAB Simulink, and their performance was evaluated under real environmental conditions with fluctuating irradiance and temperature. The results demonstrate that conventional techniques, such as incremental conductance (INC), Perturb and Observe (P&O), Incremental conductance and Particle Swam Optimization (INC‐PSO), Fuzzy Logic Control and Particle Swam Optimization (FLC‐PSO), and Perturb and Observe and Particle Swam Optimization (P&O‐PSO), achieved accuracies of 94%, 97.6%, 98.9%, 98.7%, and 99.3% respectively. In contrast, AI‐based intelligent techniques, including Artificial Neural Network (ANN), Artificial Neural Fuzzy Interference System (ANFIS), Fuzzy Logic Control (FLC), Particle Swam Optimization (PSO), and Artificial Neural Network and Particle Swam Optimization (ANN‐PSO), outperform achieving higher accuracies of 97.8%, 99.9%, 98.9%, 99.2%, and 99%, respectively. Compared to available research, which often reports lower accuracies for conventional techniques, our study highlights the enhanced performance of AI‐based methods. This study provides a comprehensive comparative analysis, delivering critical analysis and practical guidance for engineers and researchers in selecting the most effective MPPT controller optimized to specific environmental conditions. By improving the efficiency and reliability of solar power systems, our research supports the advancement of sustainable energy solutions.

show abstract

Conventional and artificial intelligence based maximum power point tracking techniques for efficient solar power generation

Khan,

Raza,

Faheem

et al. 2024

Engineering Reports

View full text Add to dashboard Cite

show abstract

Flexible photovoltaic micro-power system enabled with a customized MPPT

Wang,

Zhang

et al. 2024

Applied Energy

View full text Add to dashboard Cite

Combining dynamic adaptive snake algorithm with perturbation and observation for MPPT in PV systems under shading conditions

Mai,

Zhang,

2024

Applied Soft Computing

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Real-time implementation of a novel MPPT control based on the improved PSO algorithm using an adaptive factor selection strategy for photovoltaic systems

Cited by 14 publications

References 12 publications

Conventional and artificial intelligence based maximum power point tracking techniques for efficient solar power generation

Conventional and artificial intelligence based maximum power point tracking techniques for efficient solar power generation

Flexible photovoltaic micro-power system enabled with a customized MPPT

Combining dynamic adaptive snake algorithm with perturbation and observation for MPPT in PV systems under shading conditions

Contact Info

Product

Resources

About