• DocumentCode
    3581517
  • Title

    Photovoltaic module and maximum power point tracking modelling using Adaptive Neuro-Fuzzy Inference System

  • Author

    Tjahjono, Anang ; Qudsi, Ony Asraul ; Windarko, Novie Ayub ; Anggriawan, Dimas Okky ; Priyadi, Ardyono ; Purnomo, Mauridhi Hery

  • Author_Institution
    Dept. of Electr. Eng., Politek. Elektronika Negeri Surabaya, Surabaya, Indonesia
  • fYear
    2014
  • Firstpage
    14
  • Lastpage
    19
  • Abstract
    This paper proposes an intelligent control method using Adaptive Neuro-Fuzzy Inference System (ANFIS) for maximum power point tracking (MPPT) of PV module. The method is verified under several irradiance and temperature conditions. DC - DC boost converter is connected between the PV module and the load. Duty cycle of DC - DC boost converter is controlled by ANFIS in order to obtain the MPPT. The ANFIS directly takes operating power and voltage level as input. The proposed system is developed under Simulink-Matlab and the system of PV is simulated in PSIM to verify the effectiveness of method. The results show the proposed method can obtain the highest output power than Fuzzy Logic (FL) and Perturbation and Observation (P&O) method i.e., 30.893 and 42.973 for irradiance is 750W/m2 and 1000W/m2, respectively.
  • Keywords
    intelligent control; maximum power point trackers; power system control; solar cells; ANFIS; DC-DC boost converter; MPPT; PSIM; Simulink-Matlab; adaptive neuro-fuzzy inference system; fuzzy logic; intelligent control; maximum power point tracking modelling; perturbation and observation; photovoltaic module; Load modeling; ANFIS; DC-DC boost converter; MPPT; Photovoltaic module;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electrical Engineering and Informatics (MICEEI), 2014 Makassar International Conference on
  • Print_ISBN
    978-1-4799-6725-4
  • Type

    conf

  • DOI
    10.1109/MICEEI.2014.7067301
  • Filename
    7067301