Design Of Single And Dual-Axis Solar Tracker System Using Neural Network

Ngo, Hea Choon and Hashim, Ummi Rabaah and Raja Ikram, Raja Rina and Salahuddin, Lizawati and Cheong, Han Jie (2020) Design Of Single And Dual-Axis Solar Tracker System Using Neural Network. International Journal of Advanced Trends in Computer Science and Engineering, 9 (5). pp. 7992-7997. ISSN 2278-3091

Text 2020, NGO, SOLAR TRAKER SYSTEM - IJATCSE_02.PDF Download (875kB)

Abstract

Nowadays, solar energy is becoming more popular as it promotes awareness of an eco-friendly lifestyle. However, solar power generation is insufficient as the primary power generation in Malaysia. To overcome this issue, many private companies are devoting themselves to research to increase the amount of electricity produced by solar panels. Research has shown that a surface perpendicular to direct sunlight increases power generation significantly. Therefore, positioning the solar panel perpendicular to the direct sunlight is crucial as there are many factors to be considered. We will study the difference in dimensional movement and the method of tracking sunlight to make solar panels more efficient in power generation. Light can come from omnidirectional, the movement of the robot must have a higher dimension. Finding the highest intensity of sunlight becomes a challenges because sunlight does not constantly stagnate in a particular location. Therefore, a static solar panel is not as useful as a dynamic solar panel. Besides, the positioning of the sun will change accordingly based on the season. Hence, we need a dynamic solar tracker that can changes its solar panel position as the sun rises or falls. A neural network is chosen as the method of positioning the solar panel by moving with single-axis and moving with dual-axis.

Item Type:	Article
Uncontrolled Keywords:	Dual-axis, Neural network, Single-axis, Solar tracker
Divisions:	Faculty of Information and Communication Technology
Depositing User:	Sabariah Ismail
Date Deposited:	20 Apr 2021 12:41
Last Modified:	20 Apr 2021 12:41
URI:	http://eprints.utem.edu.my/id/eprint/25012
Statistic Details:	View Download Statistic

Actions (login required)

View Item