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• W2805445561 abstract "Photovoltaic (PV) energy is a free energy used as an alternative to fossil fuel energy. However, PV system without maximum power point tracking (MPPT) produces a low, unstable power and with a long energy payback time (EPBT). This paper presents an innovative artificial neuro-fuzzy inference system (ANFIS) MPPT technique that could extract maximum power from a complete PV system and with a lessened energy payback time (EPBT). To confirm the effectiveness of the ANFIS algorithm, its results was compared with the results of PV system using Perturb&Observe (P&O) technique, non-MPPT technique, combination of artificial neural network and support vector machine as ANN-SVM technique and using Pretoria city weather data as case studies. Results show that ANFIS-MPPT yielded the best result and with the lowest EPBT." @default.
• W2805445561 created "2018-06-13" @default.
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• W2805445561 date "2018-03-01" @default.
• W2805445561 modified "2023-09-27" @default.
• W2805445561 title "Use of MPPT techniques to reduce the energy pay-back time in PV systems" @default.
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• W2805445561 doi "https://doi.org/10.1109/irec.2018.8362553" @default.
• W2805445561 hasPublicationYear "2018" @default.
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