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

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 c...

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Bibliographic Details
Published inInternational journal of sustainable energy Vol. 31; no. 6; pp. 383 - 398
Main Authors Abu-Rub, H., Iqbal, A., Ahmed, Sk. M.
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 01.12.2012
Taylor & Francis Ltd
Subjects
adaptive neuro-fuzzy inference system (ANFIS)
boost converter
Electricity generation
maximum power point tracker
Photovoltaic cells
PI controller
Radiation
Solar energy
solar PV
Temperature
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Summary: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.
ISSN:1478-6451
1478-646X
DOI:10.1080/1478646X.2011.587517