Adaptive neuro-fuzzy inference system-based maximum power point tracking of solar PV modules for fast varying solar radiations

H. Abu-Rub, A. Iqbal, Sk M. Ahmed

Research output: Contribution to journalArticle

21 Citations (Scopus)


This paper analyses the operation of an adaptive neuro-fuzzy inference system (ANFIS)-based maximum power point tracking (MPPT) for solar photovoltaic (SPV) energy generation system. The MPPT works on the principle of adjusting the voltage of the SPV modules by changing the duty ratio of the boost converter. The duty ratio of the boost converter is calculated for a given solar irradiance and temperature condition by a closed-loop control scheme. The ANFIS is trained to generate maximum power corresponding to the given solar irradiance level and temperature. The response of the ANFIS-based control system is highly precise and offers an extremely fast response. The response time is seen as nearly 1 ms for fast varying cell temperature and 6 ms for fast varying solar irradiance. The simulation is done for fast-changing solar irradiance and temperature conditions. The response of the proposed controller is also presented.

Original languageEnglish
Pages (from-to)383-398
Number of pages16
JournalInternational Journal of Sustainable Energy
Issue number6
Publication statusPublished - 1 Dec 2012



  • PI controller
  • adaptive neuro-fuzzy inference system (ANFIS)
  • boost converter
  • maximum power point tracker
  • solar PV

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Fuel Technology
  • Energy(all)
  • Process Chemistry and Technology
  • Fluid Flow and Transfer Processes

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