Adaptive neuro-fuzzy inference system based maximum power point tracking of a solar PV module

Author

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

Author_Institution

Dept. of Electr. & Comput. Eng., Texas A&M Univ. at Qatar, Doha, Qatar

fYear

2010

fDate

18-22 Dec. 2010

Firstpage

51

Lastpage

56

Abstract

This paper presents and analyses the operation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) based maximum power point tracker (MPPT) for solar photovoltaic (SPV) energy generation system. The MPPT works on the principle of adjusting the voltage of the solar PV modules by changing the duty ratio of the boost converter. The duty ratio of boost converter is calculated for given solar irradiance and temperature condition by a closed-loop control scheme. The ANFIS is trained to generate the maximum power corresponding to the given solar irradiance level and temperature. The response of ANFIS based control system is highly precise and offers very fast response. Simulation results are provided to validate the concept.

Keywords

adaptive control; closed loop systems; fuzzy control; fuzzy neural nets; fuzzy reasoning; maximum power point trackers; neurocontrollers; photovoltaic power systems; power convertors; power generation control; solar power stations; ANFIS based control system; MPPT; adaptive neuro-fuzzy inference system; closed-loop control scheme; maximum power point tracking; solar PV module; solar photovoltaic energy generation system; Algorithm design and analysis; Artificial intelligence; Mathematical model; Power generation; Renewable energy resources; Short circuit currents; Temperature control; Adaptive Neuro-Fuzzy Inference System (ANFIS); Maximum Power Point tracker; Solar PV; boost converter;

fLanguage

English

Publisher

ieee

Conference_Titel

Energy Conference and Exhibition (EnergyCon), 2010 IEEE International

Conference_Location

Manama

Print_ISBN

978-1-4244-9378-4

Type

conf

DOI

10.1109/ENERGYCON.2010.5771737

Filename

5771737