Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Maximum Power Point Tracking for Brushless DC Motor Driven Photovoltaic Pumping System Using Hybrid ANFIS-FLOWER Pollination Optimization Algorithm

Neeraj Priyadarshi
* ,
Sanjeevikumar Padmanaban
* ORCID logo ,
Lucian Mihet-Popa
* ORCID logo ,
Frede Blaabjerg
ORCID logo ,
Farooque Azam
Version 1 : Received: 18 March 2018 / Approved: 19 March 2018 / Online: 19 March 2018 (11:04:32 CET)

A peer-reviewed article of this Preprint also exists.

Priyadarshi, N.; Padmanaban, S.; Mihet-Popa, L.; Blaabjerg, F.; Azam, F. Maximum Power Point Tracking for Brushless DC Motor-Driven Photovoltaic Pumping Systems Using a Hybrid ANFIS-FLOWER Pollination Optimization Algorithm. Energies 2018, 11, 1067. Priyadarshi, N.; Padmanaban, S.; Mihet-Popa, L.; Blaabjerg, F.; Azam, F. Maximum Power Point Tracking for Brushless DC Motor-Driven Photovoltaic Pumping Systems Using a Hybrid ANFIS-FLOWER Pollination Optimization Algorithm. Energies 2018, 11, 1067.

Abstract

In this research paper, a hybrid Artificial Neural Network (ANN)-Fuzzy Logic Control (FLC) tuned Flower Pollination Algorithm (FPA) as a Maximum Power Point Tracker (MPPT) is employed to emend root mean square error (RMSE) of photovoltaic (PV) modeling. Moreover, Gaussian membership functions have been considered for fuzzy controller design. This paper interprets Luo converter occupied brushless DC motor (BLDC) directed PV water pump application. Experimental responses certify the effectiveness of the suggested motor-pump system supporting diverse operating states. Luo converter is newly developed dc-dc converter has high power density, better voltage gain transfer and superior output waveform and able to track optimal power from PV modules. For BLDC speed controlling there is no extra circuitry and phase current sensors are enforced for this scheme. The recentness of this attempt is adaptive neuro-fuzzy inference system (ANFIS)-FPA operated BLDC directed PV pump with advanced Luo converter has not been formerly conferred.

Keywords

ANFIS; artificial neural network; brushless DC motor; FPA; maximum power point tracking; photovoltaic system; root mean square error

Subject

Engineering, Electrical and Electronic Engineering

Copyright: 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.

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