Maximum power point tracking improving of photovoltaic systems based on hybrid triangulation topology aggregation optimizer and incremental conductance algorithm

• Datum: 2025
• Hauptverfasser: Jeridi, A., Moulahi, M. H., Khaterchi, H., Zaafouri, A.
• Format: Artikel
• Sprache: English
• Veröffentlicht: National Technical University "Kharkiv Polytechnic Institute" and Аnatolii Pidhornyi Institute of Power Machines and Systems of NAS of Ukraine 2025
• Schlagworte: solar photovoltaic system; triangulation topology aggregation optimizer; maximum power point tracking; global maximum power point; partial shading conditions
• Online Zugang: http://eie.khpi.edu.ua/article/view/320320
• Назва журналу: Electrical Engineering & Electromechanics

Introduction. Maximum power point tracking (MPPT) in photovoltaic (PV) systems has been a key research focus in recent years. While numerous techniques have been proposed to optimize power extraction, each suffers from inherent limitations that hinder their effectiveness. Problem. Environmental factors such as shading, partial shading, and low irradiance levels significantly impact PV system performance, with partial shading being the most critical and complex challenge due to its creation of multiple local power maxima. Goal. This study aims to improve MPPT in PV systems under partial shading conditions by developing a hybrid approach that integrates a Triangulation Topology Aggregation Optimizer (TTAO) with the Incremental Conductance (IC) algorithm. Methodology. Simulations were conducted in MATLAB/Simulink under four static partial shading scenarios, comparing the hybrid TTAO-IC algorithm against traditional methods like Perturb and Observe (P&O), IC and metaheuristic algorithms. Scientific novelty of this work lies in the hybrid TTAO-IC algorithm, which combines the global optimization strength of TTAO with the precision of IC, addressing the shortcomings of conventional methods. Practical value. The results show that the hybrid TTAO-IC algorithm achieves tracking efficiencies exceeding 99 %, outperforming existing methods and demonstrating robust adaptability to varying environmental conditions. References 31, tables 5, figures 15.