Simulation and Analyze of Global MPPT Based on Hybrid Classical-ANN with PSO Learning Approach for PV System

  • Ihssane ChtoukiEmail author
  • Patrice Wira
  • Malika Zazi
  • Houssam Eddine Chakir
  • Bruno Collicchio
Conference paper
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 7)


This paper discusses the optimization efficiency of the Maximum Power Point Tracking (MPPT) algorithms in solar photovoltaic (PV) systems. In fact, to extract the maximum power, the control of the photovoltaic source must meet the requirements of maximum energy performance and reasonable cost (calculation time and simplicity of implementation). To do this, two new MPPT algorithms based on the Perturb and Observe (P&O) method with a fixed step are proposed. The two suggested controllers are used to improve the drawbacks of the conventional P&O method. Indeed to addressing this problem special techniques derived from artificial intelligence are implemented. The first one uses an Artificial Neural Network (ANN) with Levenberg Marquard (LM) learning algorithm as a neural regulator named (POPI-LMNN). The second one uses an Evolutionary Neural Network (ENN) with a Partical Swarms Optimization (PSO) tuning approach as an evolutionary neural regulator named (POPI-PSONN). The controllers applied to the Boost are used as a powerful impedance adaptation and allows an optimal transfer of energy from the solar panels to the loads. A Simulink model is built to simulate the proposed MPPT methods. To better clarify our contribution, a comparative study with the P&O technique is carried out. The simulation results confirm the advantageous contribution of the new “POPI-PSONN”algorithm during sudden changes in solar illumination characterized by its simplicity, its speed of search of the MPP and its independence of the solar panel parameters, as well as its ability to eliminate oscillations. Furthermore, the combination of learning neural networks with PSO solves the problem of convergence towards a global optimum all this gives it very high reliability.


Photovoltaic systems P&O Artificial Neural Network Levenberg Marquard Evolutionary Neural Network Partical Swarms Optimization 


  1. 1.
    Lasheen, M., Salam, M.A.: Maximum power point tracking using Hill Climbing and ANFIS techniques for PV applications: a review and a novel hybrid approach. Energy Convers. Manag. 171, 1002–1019 (2018)CrossRefGoogle Scholar
  2. 2.
    Boualem, B., Belmili, H., Fateh, K.: A survey of the most used MPPT methods: conventional and advanced algorithms applied for photovoltaic systems. Renew. Sustain. Energy Rev. 45, 637–648 (2015)CrossRefGoogle Scholar
  3. 3.
    Ben Salah, C., Ouali, M.: Comparison of fuzzy logic and neural network in maximum power point tracker for PV systems. Electric Power Syst. Res. 81(1), 43–50 (2011)CrossRefGoogle Scholar
  4. 4.
    Pillai, D.S., Rajaseka, N.: Metaheuristic algorithms for PV parameter identification: a comprehensive review with an application to threshold setting for fault detection in PV systems. Renew. Sustain. Energy Rev. 82(3), 3503–3525 (2018)CrossRefGoogle Scholar
  5. 5.
    Seyed Mahmoudian, M., Mohamadi, A., Kumary, S., Maung, A., Oo, T., Stojcevski, A.: A comparative study on procedure and state of the art of conventional maximum power point tracking techniques for photovoltaic system. Int. J. Comput. Electr. Eng. 6(5), 402 (2014)CrossRefGoogle Scholar
  6. 6.
    Smith, J.S., Wu, B., Wilamowski, B.M.: Neural network training with Levenberg–Marquardt and adaptable weight compression. IEEE Trans. Neural Networks Learn. Syst. 30(2), 580–587 (2019)CrossRefGoogle Scholar
  7. 7.
    Chan, K.Y., Dillon, T.S., Singh, J., Chang, E.: Neural-network-based models for short-term traffic flow forecasting using a hybrid exponential smoothing and Levenberg–Marquardt algorithm. IEEE Trans. Intell. Transp. Syst. 13(2), 644–654 (2012)CrossRefGoogle Scholar
  8. 8.
    Tanweera, M.R., Suresha, S., Sundararajan, N.: Self-regulating particle swarm optimization algorithm. Inf. Sci. 294, 182–202 (2015)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Pareja, M.: Pv cell simulation with Qucs, a generic model of pv cell 20(07) (2013)Google Scholar
  10. 10.
    Chtouki, I., Wira, P., Zazi, M.: Comparison of several neural network perturb and observe MPPT methods for photovoltaic applications. In: The 19th International Conference on Industrial Technology (ICIT 2018) lyon, France, pp. 1–6 (2018)Google Scholar
  11. 11.
    Özgür, C., Teke, A.: A Hybrid MPPT method for grid connected photovoltaic systems under rapidly changing atmospheric and Technology. Electric Power Syst. 152, 194–210 (2017)CrossRefGoogle Scholar
  12. 12.
    Bishop, C.M.: Neural Networks for Pattern Recognition. Clarendon Press (1995)Google Scholar
  13. 13.
    Abedinia, O., Amjady, N., Ghadimi, N.: Solar energy forecasting based on hybrid neural network and improved metaheuristic algorithm. Comput. Intell. 34(1), 241–260 (2017)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Aljarah, I., Faris, H., Mirjalili, S.: Optimizing connection weights in neural networks using the whale optimization algorithm. Soft Comput. 18(5) (2017) Google Scholar
  15. 15.
    Gürüler, H., Peker, M., Baysa, Ö.: Soft computing model on genetic diversity and pathotype differentiation A novel approach. Electronic J. Biotechnol. 22(1) (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ihssane Chtouki
    • 1
    Email author
  • Patrice Wira
    • 2
  • Malika Zazi
    • 1
  • Houssam Eddine Chakir
    • 3
  • Bruno Collicchio
    • 2
  1. 1.ERRER LabMohammed V University, ENSETRabatMorocco
  2. 2.IRIMAS LabHaute Alsace UniversityMulhouseFrance
  3. 3.PMMAT Lab, Faculty of ScienceHassan II UniversityCasablancaMorocco

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