@inproceedings{34aad8f4ac9041e8ac20bdf61c6b612c,
title = "Photovoltaic module and maximum power point tracking modelling using Adaptive Neuro-Fuzzy Inference System",
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 = "ANFIS, DC-DC boost converter, MPPT, Photovoltaic module",
author = "Anang Tjahjono and Qudsi, {Ony Asraul} and Windarko, {Novie Ayub} and Anggriawan, {Dimas Okky} and Ardyono Priyadi and Purnomo, {Mauridhi Hery}",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 Makassar International Conference on Electrical Engineering and Informatics, MICEEI 2014 ; Conference date: 26-11-2014 Through 30-11-2014",
year = "2014",
month = mar,
day = "24",
doi = "10.1109/MICEEI.2014.7067301",
language = "English",
series = "Proceeding - 2014 Makassar International Conference on Electrical Engineering and Informatics, MICEEI 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "14--19",
editor = "Elyas Palantei",
booktitle = "Proceeding - 2014 Makassar International Conference on Electrical Engineering and Informatics, MICEEI 2014",
address = "United States",
}
