Global maximum power point tracking method for photovoltaic systems using Takagi-Sugeno fuzzy models and ANFIS approach

• Datum: 2025
• Hauptverfasser: Hadjidj, N., Benbrahim, M., Ounnas, D., Mouss, L. H.
• 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: photovoltaic system; maximum power point tracking; Takagi-Sugeno fuzzy model; linear matrix inequalities
• Online Zugang: http://eie.khpi.edu.ua/article/view/310475
• Назва журналу: Electrical Engineering & Electromechanics

Introduction. A new global maximum power point tracking (GMPPT) control strategy for a solar photovoltaic (PV) system, based on the combination of Takagi-Sugeno (T-S) fuzzy models and an ANFIS, is presented. The novelty of this paper lies in the integration of T-S fuzzy models and the ANFIS approach to develop an efficient GMPPT controller for a PV system operating under partial shading conditions. Purpose. The new GMPPT control strategy aims to extract maximum power from the PV system under varying weather conditions or partial shading. Methods. An ANFIS algorithm is used to determine the maximum voltage, which corresponds to the actual maximum power point, based on PV voltage and current. Next, the nonlinear model of the PV system is employed to design the T-S fuzzy controller. A reference model is then derived based on the maximum voltage. Finally, a tracking controller is developed using the reference model and the T-S fuzzy controller. The stability of the overall system is evaluated using Lyapunov’s method and is represented through linear matrix inequalities expressions. The results clearly demonstrate the advantages of the proposed GMPPT-based fuzzy control strategy, showcasing its high performance in effectively reducing oscillations in various steady states of the PV system, ensuring minimal overshoot and a faster response time. In addition, a comparative analysis of the proposed GMPPT controller against conventional algorithms, such as Incremental Conductance, Perturb & Observe and Particle Swarm Optimization, shows that it offers a fast dynamic response in finding the maximum power with significantly less oscillation around the maximum power point. References 20, tables 3, figures 14.