A comprehensive study of recent maximum power point tracking techniques for photovoltaic systems
Mohammed Hamouda Ali1, Mohammad Zakaria2, Sally El-Tawab2
1Department of Electrical Engineering, Faculty of Engineering, Al-Azhar University, Cairo, 11651, Egypt. eng_mohammedhamouda@azhar.edu.eg.
Scientific Reports
|April 24, 2025
Summary
Artificial intelligence techniques offer nearly 99% tracking efficiency for solar photovoltaic (PV) systems, outperforming conventional and metaheuristic methods. This study guides MPPT selection for optimal PV energy generation.
Area of Science:
- Renewable Energy Systems
- Photovoltaic Energy Conversion
- Power Electronics Control
Background:
- Global renewable energy is increasing, with solar photovoltaics (PV) leading capacity expansion due to availability, safety, and environmental benefits.
- Maximizing energy yield from PV systems is critical due to inherent limitations in cell efficiency and environmental variability.
- Maximum Power Point Tracking (MPPT) strategies are essential for optimizing power output in PV systems.
Purpose of the Study:
- To model and develop advanced MPPT techniques for enhanced PV system performance.
- To critically analyze and compare various MPPT methodologies based on key performance indicators.
- To provide a guideline for selecting optimal MPPT methods for diverse environmental conditions using multi-criteria decision-making.
Main Methods:
- Investigated five MPPT techniques: conventional (Perturb & Observe, Incremental Conductance), metaheuristic (Particle Swarm Optimization), and artificial intelligence (Artificial Neural Network, Fuzzy Logic Control).
- Evaluated techniques based on tracking speed, algorithm complexity, and dynamic tracking under varying environmental conditions.
- Employed Multi-Criteria Decision-Making (MCDM) using AHP, CRITIC, and VIKOR for comparative analysis and ranking based on effectiveness and economic feasibility.
Main Results:
- Artificial intelligence (AI) techniques demonstrated superior tracking efficiency, achieving approximately 99%.
- AI methods exhibited faster and more efficient tracking speeds compared to conventional and metaheuristic approaches.
- The study provides a ranked comparison of MPPT methods, highlighting AI's advantages in performance and speed.
Conclusions:
- AI-based MPPT techniques are highly effective for maximizing solar PV energy generation.
- The comprehensive analysis and MCDM approach offer valuable insights for selecting appropriate MPPT strategies.
- This research serves as a crucial reference for optimizing PV system operation and economic viability.
Keywords:
Artificial intelligence (AI)Incremental conductance (INC)MCDMMPPTPV systemParticle swarm optimization (PSO)Perturb and observation (P&O)More Related Videos
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