An Online Self Recurrent Direct Adaptive Neuro-Fuzzy Wavelet Based Control of Photovoltaic Systems

  • Syed Zulqadar HassanEmail author
  • Tariq Kamal
  • Sidra Mumtaz
  • Laiq Khan
Part of the Power Systems book series (POWSYS)


Solar through photovoltaic is an inexhaustible energy source which contributes to enhance the sustainability of the society. Though, photovoltaic systems experience some fundamental problems such as low conversion efficiency particularly during high weather variations and the high nonlinearity between the photovoltaic output power and current. These problems involve in photovoltaic systems need the use of advanced intelligent control methods. This book chapter develops a new direct adaptive maximum power point tracking control for photovoltaic systems. The new proposed technique integrates a Chebyshev wavelet in the consequent part of existing neuro-fuzzy structure. The parameters of the proposed controller are tuned adaptively online using backpropagation algorithm. The performance of the proposed method is tested under high uncertainties appearing from solar irradiance, temperature and fluctuations in load. Finally, simulation results are provided to show that the proposed control method is better than other existing methods in terms of efficiency, load tracking and output power.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Syed Zulqadar Hassan
    • 1
    Email author
  • Tariq Kamal
    • 2
    • 3
  • Sidra Mumtaz
    • 4
  • Laiq Khan
    • 4
  1. 1.School of Electrical EngineeringChongqing UniversityChongqingChina
  2. 2.Faculty of Engineering, Department of Electrical and Electronics EngineeringSakarya UniversitySakaryaTurkey
  3. 3.Research Group in Electrical Technologies for Sustainable and Renewable Energy (PAIDI-TEP023), Department of Electrical Engineering, Higher Polytechnic School of AlgecirasUniversity of CadizAlgecirasSpain
  4. 4.Department of Electrical EngineeringCOMSATS UniversityIslamabadPakistan

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