Optimizing the performance of a stand-alone PV system under non-uniform irradiance using Gray-Wolf and hybrid neural network AI-MPPT algorithms

Authors

Merna M. Eshak, Electrical Engineering Department, Faculty of Engineering and FAbLab in the Center for Emerging Learning Technology(CELT), The British University in Egypt (BUE), Cairo, Egypt
Mohamed A. Khafagy, Electrical Engineering Department, MSc. Student in Renewable Energy Program, Faculty of Engineering, The British University of Egypt (BUE), Cairo, Egypt
Peter Makeen, The British University in Egypt;Follow
Sameh O. Abdellatif, Electrical Engineering, the British University in Egypt (BUE)

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