Title: Enhancing energy and exergy efficiencies in smart grid integration with a hybrid DHO-MACNN technique for photovoltaic system

Authors: C. Karuppasamy; R. Karpaga Priya; M. Ramuvel

Addresses: Department of Electrical and Electronics Engineering, AAA College of Engineering and Technology, Sivakasi – 626 005, India ' Department of Electrical and Electronics Engineering, Saveetha Engineering College, Chennai – 602 105, India ' Department of Electrical and Electronics Engineering, AAA College of Engineering and Technology, Sivakasi, Tamilnadu-626005, India

Abstract: This manuscript proposes a hybrid technique for maximum power point tracking-based smart grid integration with photovoltaic (PV) systems, combining deer hunting optimisation (DHO) and multi-scale attention convolutional neural network. It aims to enhance system performance, efficiency, and reduce total harmonic distortion complexity. Implemented and compared using MATLAB, the proposed approach surpasses existing methods like Harris Hawk optimisation (HHO), genetic algorithm (GA), and artificial neural network (ANN), notably achieving lower THD levels. These findings underscore the method's efficacy in optimising energy and exergy efficiencies, marking a significant advancement in PV system integration into modern smart grids.

Keywords: maximum power point tracking; smart grid; photovoltaic system; DC-DC boost converter; load current; grid current; power loss; total harmonic distortion; THD.

DOI: 10.1504/IJEX.2024.141694

International Journal of Exergy, 2024 Vol.45 No.1/2, pp.109 - 125

Received: 08 Apr 2024
Accepted: 18 Jun 2024
Published online: 30 Sep 2024 *

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