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Energies 2018, 11(3), 606; https://doi.org/10.3390/en11030606

Adaptive Feedback Linearization Based NeuroFuzzy Maximum Power Point Tracking for a Photovoltaic System

1
Department of Electrical Engineering, COMSATS Institute of Information Technology, Abbottabad 22060, Pakistan
2
Department of Electrical and Electronics Engineering, Faculty of Engineering, Sakarya University, Serdivan 54050, Sakarya, Turkey
3
Department of Electrical Engineering, Higher Polytechnic School of Algeciras, University of Cadiz, 11202 Algeciras, Spain
4
Department of Power System and Its Automation, Chongqing University, Chongqing 400044, China
*
Author to whom correspondence should be addressed.
Received: 17 January 2018 / Revised: 1 March 2018 / Accepted: 6 March 2018 / Published: 9 March 2018
(This article belongs to the Special Issue Sustainable and Renewable Energy Systems)
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Abstract

In the current smart grid scenario, the evolution of a proficient and robust maximum power point tracking (MPPT) algorithm for a PV subsystem has become imperative due to the fluctuating meteorological conditions. In this paper, an adaptive feedback linearization-based NeuroFuzzy MPPT (AFBLNF-MPPT) algorithm for a photovoltaic (PV) subsystem in a grid-integrated hybrid renewable energy system (HRES) is proposed. The performance of the stated (AFBLNF-MPPT) control strategy is approved through a comprehensive grid-tied HRES test-bed established in MATLAB/Simulink. It outperforms the incremental conductance (IC) based adaptive indirect NeuroFuzzy (IC-AIndir-NF) control scheme, IC-based adaptive direct NeuroFuzzy (IC-ADir-NF) control system, IC-based adaptive proportional-integral-derivative (IC-AdapPID) control scheme, and conventional IC algorithm for a PV subsystem in both transient as well as steady-state modes for varying temperature and irradiance profiles. The comparative analyses were carried out on the basis of performance indexes and efficiency of MPPT. View Full-Text
Keywords: photovoltaic (PV); maximum power point tracking (MPPT); NeuroFuzzy; feedback linearization; hybrid renewable energy system (HRES) photovoltaic (PV); maximum power point tracking (MPPT); NeuroFuzzy; feedback linearization; hybrid renewable energy system (HRES)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Mumtaz, S.; Ahmad, S.; Khan, L.; Ali, S.; Kamal, T.; Hassan, S.Z. Adaptive Feedback Linearization Based NeuroFuzzy Maximum Power Point Tracking for a Photovoltaic System. Energies 2018, 11, 606.

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