Global Maximum Power Point Tracking Based on Intelligent Approach for Photovoltaic System Under Partial Shading Conditions

  • Moulay Rachid DouiriEmail author
  • Sidi Mohamed Douiri
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10632)


This paper presents the design of a controller for Maximum Power Point Tracking (MPPT) of a photovoltaic system. The proposed controller relies upon a Recurrent Neuro-Fuzzy (RNF) which is designed as a combination of the concepts of Sugeno fuzzy model and neural network. The controller employs the RNF of four-layer with sixty-four fuzzy rules. Moreover, for the proposed RNF an improved self-tuning method is developed based on the photovoltaic system and its high performance requirements. The principal task of the tuning method is to adjust the parameters of the Fuzzy Logic (FL) in order to minimize the square of the error between actual and reference output. Simulations with practical parameters show that our proposed MPPT using RNF outperform the conventional MPPT controller terms of tracking speed and accuracy.


Maximum power point tracking Photovoltaic system Recurrent Neuro-Fuzzy 


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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Electrical Engineering, Higher School of TechnologyCadi Ayyad UniversityEssaouiraMorocco
  2. 2.Laboratory of Mathematic Informatics and Applications, Faculty of SciencesUniversity Mohammed V-AgdalRabatMorocco

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