Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

A Novel MPPT based reptile search algorithm for photovoltaic system under various conditions

Douifi, Nadia, Abbadi, Amel, Hamidia, Fethia, Yahya, Khalid, Mohamed, Mahmoud ORCID: https://orcid.org/0000-0001-9386-7495 and Rai, Nawal 2023. A Novel MPPT based reptile search algorithm for photovoltaic system under various conditions. Applied Sciences 13 (8) , 4866. 10.3390/app13084866

[thumbnail of applsci-13-04866-v2.pdf] PDF - Published Version
Download (4MB)

Abstract

Solar systems connected to the grid are crucial in addressing the global energy crisis and meeting rising energy demand. The efficiency of these systems is totally impacted by varying weather conditions such as changes in irradiance and temperature throughout the day. Additionally, partial shading (PS) adds to the complexity of the nonlinear characteristics of photovoltaic (PV) systems, leading to significant power loss. To address this issue, maximum power point tracking (MPPT) algorithms have become an essential component in PV systems to ensure optimal power extraction. This paper introduces a new MPPT control technique based on a novel reptile search optimization algorithm (RSA). The effectiveness of the proposed RSA method is evaluated under different weather conditions with varying irradiance and partial shading. The results of the RSA algorithm are compared to other existing bio-inspired algorithms and show superior performance with an average efficiency of 99.91%, faster dynamic response of 50 ms, and less than 20 watts of oscillation. The RSA-MPPT based technique provides higher efficiency, faster settling time, and minimal oscillation around the maximum power point (MPP), making it a reliable solution for effective solar power harvesting.

Item Type: Article
Date Type: Published Online
Status: Published
Schools: Engineering
Additional Information: License information from Publisher: LICENSE 1: URL: https://creativecommons.org/licenses/by/4.0/, Type: open-access
Publisher: MDPI
ISSN: 2076-3417
Date of First Compliant Deposit: 9 May 2023
Date of Acceptance: 11 April 2023
Last Modified: 10 May 2023 04:41
URI: https://orca.cardiff.ac.uk/id/eprint/159325

Citation Data

Cited 2 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics