Optimization of photovoltaic energy harvesting using clonal selection algorithm

Tan, Min Keng and Grace Butiza Joponi and Lim and Ahmad Razani Haron and Chai, Chang-Yii and Tze, Kenneth Kin Teo (2023) Optimization of photovoltaic energy harvesting using clonal selection algorithm.

	Text ABSTRACT.pdf Download (38kB)
	Text FULL TEXT.pdf Restricted to Registered users only Download (1MB)

Abstract

The rising demand for renewable energy sources has fueled extensive research in photovoltaic (PV) systems. However, conventional Maximum Power Point Tracking (MPPT) algorithms often encounter challenges when tracking the global maximum power point under non-uniform irradiance conditions. To address this issue, the Clonal Selection Algorithm (CSA) is proposed as an effective approach to enhance MPPT algorithm performance. The CSA dynamically adjusts the voltage perturbation size based on instant ambient irradiance and temperature, leading to improved global maximum power point tracking and enhanced efficiency in PV systems. Experimental results demonstrate the superiority of the proposed CSA over conventional MPPT algorithms, especially in scenarios with varying solar irradiance. The CSA's adaptability allows PV systems to operate closer to their optimal efficiency, maximizing energy harvest from available solar resources. Overall, this research contributes valuable insights into sustainable and efficient energy solutions by leveraging the capabilities of the CSA. Successfully integrating the CSA in PV systems plays a critical role in establishing an eco-friendly and resilient renewable energy infrastructure, for a greener future.

Item Type:	Proceedings
Keyword:	Photovoltaic energy optimization, Clonal selection algorithm, Maximum power point tracking
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1-9971 Electrical engineering. Electronics. Nuclear engineering
Department:	FACULTY > Faculty of Engineering
Depositing User:	SITI AZIZAH BINTI IDRIS -
Date Deposited:	06 Nov 2024 14:20
Last Modified:	08 Nov 2024 10:54
URI:	https://eprints.ums.edu.my/id/eprint/41762

Actions (login required)

View Item