Optimizing the performance of a stand-alone PV system under non-uniform irradiance using Gray-Wolf and hybrid neural network AI-MPPT algorithms
Document Type
Article
Publication Date
11-18-2020
Abstract
This paper introduces an improved gray-wolf optimization technique (EGWO) for a photovoltaic (PV) stand-alone system. The fundamental objective is to study non-uniform solar irradiance power mismatches in PV modules through modelling maximum power point tracker (MPPT) for increasing PV power output. An EGWO-MPPT detection algorithm for promoting the global peak between the multiple peaks is implemented, seeking for the optimum maximum energy from the PV system. Furthermore, a neural network-based MPPT optimizer has been modeled as a benchmark for our proposed system, showing the trade-off between time response and accuracy under a non-uniform irradiance profile.
Recommended Citation
M. Eshak, Merna; Khafagy, Mohamed A.; Makeen, Peter; and Abdellatif, Sameh O., "Optimizing the performance of a stand-alone PV system under non-uniform irradiance using Gray-Wolf and hybrid neural network AI-MPPT algorithms" (2020). Electrical Engineering. 91.
https://buescholar.bue.edu.eg/elec_eng/91