• NORA KACIMI Process Control Laboratory, National Polytechnic School, Algiers
  • ABDELHAKIM IDIR Department of Electrical Engineering, University Mohamed Boudiaf of M'sila, Algeria
  • SAID GROUNI University M’Hamed Bougara of Boumerdes, LAA Laboratory ,Boumerdes, Algeria.
  • MOHAMED SEGHIR BOUCHERIT University M’Hamed Bougara of Boumerdes, LAA Laboratory, Boumerdes


Global maximum power point tracking, Grasshopper optimization algorithm, Model predictive control, Photovoltaic system, Partial shading conditions


The power-voltage characteristic of photovoltaic (PV) systems operating under partial shading conditions (PSCs) exhibits multiple local maximum power points (MPPs). Conventional maximum power point tracking (MPPT) methods are effective under uniform solar irradiance conditions. Moreover, the power of PV systems may be decreased by the random fluctuation, oscillation, and slow speed of their power tracking. To overcome these problems, a new combined method based on the metaheuristic Grasshopper Optimization Algorithm (GOA) and Model Predictive Controller (MPC) is proposed. A series of experimental simulations were carried out on various cases to evaluate the performance of the proposed method and to better clarify our contribution, a comparative study with the traditional perturb and observe (P&O) method, PSO-based MPC (PSO-MPC), particle swarm optimization (PSO) method, and grasshopper optimization algorithm (GOA) was carried out. The results show that the proposed method significantly outperforms the competing methods such as PSO, PSO-MPC, and GOA regarding tracking time, power conversion efficiency, and oscillations in PV output power.


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