• 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.


(1) A.A. F Mirza, Q Ling, M.Y Javed, M Mansoor, Novel MPPT techniques for photovoltaic systems under uniform irradiance and Partial shading, Solar Energy journal, 184, pp. 628–648 (2019).

(2) A. Attou, A. Massoum, M. Chadli, Comparison study of two tracking methods for photovoltaic systems, Rev. Roum. Sci. Techn. – Electrotechn. Energ., 60, 2, pp. 205–214 (2015).

(3) B. Bendib, H. Belmili, F. Krim, A survey of the most used MPPT methods: Conventional and advanced algorithms applied for photovoltaic system, Renewable and Sustainable Energy Reviews Journal, 45, pp. 637–648 (2015).

(4) U Yilmaz, A. Kircayand, S. Borekci, PV system fuzzy logic MPPT method and PI control as a charge controller, Renewable and Sustainable Energy Reviews (81): 994–1001(2018).

(5) N. Kacimi, S.Grouni, A. Idir, M.S. Boucherit, New improved hybrid MPPT based on neural network-model predictive control-Kalman-filter for photovoltaic system, Indonesian Journal of Electrical Engineering and Computer Science, 20, 3, pp. 1230-1241 (2020).

(6) H. Deboucha, S.L. Belaid, Improved incremental conductance maximum power point tracking algorithm using fuzzy logic controller for photovoltaic system, Rev. Roum. Sci. Techn. – Electrotechn. Energ., 62, 4, pp. 381–387 (2017).

(7) S.P. Bihari, P.K. Sadhu, S. Das, P. Arvind, A. Gupta, Design and implementation of a photovoltaic wind hybrid system with the assessment of fuzzy logic maximum power point technique, Rev. Roum. Sci. Techn.– Électrotechn. et Énerg., 64, 3, pp. 235–240, (2019).

(8) H.A. Azzeddine, M. Tioursi, D.-E. Chaouch, B. Khiari, An offline trained artificial neural network to predict a photovoltaic panel maximum power point, Rev. Roum. Sci. Techn. – Electrotechn. Energ., 61, 3, pp. 255–257 (2017).

(9) S Veerapen, H. Wen, X. Li,Y. Du, Y. Yang, Y. Wang, W. Xiao, A novel global maximum power point tracking algorithm for photovoltaic system with variable perturbation frequency and zero oscillation, Solar Energy Journal, 181, pp. 345–356 (2019).

(10) T. Guan et al., Global maximum power point tracking algorithm for solar power system, Pan JS., Li J., Tsai PW., Jain L. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. Smart Innovation, Systems, and Technologies, Springer, Singapore, 157, (2020).

(11) H.D. Liu et al., A GMPPT algorithm for preventing the LMPP problems based on trend line transformation technique, Solar Energy, 198, pp. 53-67 (2020).

(12) K Sundareswaran, V. Vigneshkumar, S Palani, Application of a combined particle swarm optimization and perturb and observe method for MPPT in PV systems under partial shading conditions, Renewable Energy Journal, 75, pp. 308-317 (2015).

(13) A. Al-Gizi, A. Craciunescu, M.A. Fadel, M. Louzazni, A new hybrid algorithm for photovoltaic maximum power point tracking under partial shading conditions, Rev. Roum. Sci. Techn. – Electrotechn. Energ., 63, 1, pp. 52–57 (2018).

(14) Z. Amokrane, M. Haddadi, N.O. Cherchali, A new method of tracing the characteristic of photovoltaic generators under real operating conditions, Rev. Roum. Sci. Techn. – Electrotechn. Energ., 62, 3, pp. 276–281 (2017).

(15) C. Ahmed, M. Cherkaoui, M. Mokhlis, PSO-SMC controller based GMPPT technique for photovoltaic panel under partial shading effect, International Journal of Intelligent Engineering and Systems, 13, 2, pp. 307–316 (2020).

(16) A. Fathy, O. El-baksawi, Grasshopper optimization algorithm for extracting maximum power from wind turbine installed in Al-Jouf region, J. Renewable Sustainable Energy, 11, 033303. (2019).

(17) N.M. Amin, A.M. Soliman, H.M Hasanien, A.Y. Abdelaziz, Grasshopper optimization algorithm-based PI controller scheme for performance enhancement of a grid‑connected wind generator, J. of Control, Automation and Electrical Systems, 31, pp. 393–401, (2020).

(18) S.A. Tadjer, A. Idir, F. Chekired, Comparative performance evaluation of four photovoltaic technologies in Saharian climates of Algeria: ghardaïa pilot station, Indonesian Journal of Electrical Engineering and Computer Science, 18, 2, pp. 586-598 (2020).






Thermotechnique et thermoénergétique / Thermotechnics and Thermal Energy