Optimization Variation for Multiple Heuristic Approaches in Solar Tracking

D.F. Fam; S.P. Koh; Tiong Sieh Kiong; K.H. Chong

doi:10.4028/www.scientific.net/KEM.480-481.1085

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HomeKey Engineering MaterialsKey Engineering Materials Vols. 480-481Optimization Variation for Multiple Heuristic...

Optimization Variation for Multiple Heuristic Approaches in Solar Tracking

Abstract:

The solar tracker, a device that keeps PV or photo-thermal panels in an optimum position perpendicular to the solar radiation during daylight hours, increases the collected energy and can be considered as one of the economical renewable energy in generating electricity. The current trend in solar concentrator tracking system is to use an open-loop local controller that computes the direction of the solar vector based on geographical location and time. It is not accurate because it has error from computing the intensity variation. Literature suggested that the photovoltaic panels could produce maximum power if the panels have angle of inclination zero degree to the sun position. In this research, genetic algorithm is one of the optimization techniques used to maximize the performance of solar tracking system. Research shows that among the heuristic approaches within 50 generation, Genetic Algorithm reaches the best fitness value at 0.01706 with optimum voltage 10.05V.

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Periodical:

Key Engineering Materials (Volumes 480-481)

Pages:

1085-1090

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.480-481.1085

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Online since:

June 2011

Authors:

D.F. Fam, S.P. Koh, Tiong Sieh Kiong, K.H. Chong

Keywords:

Genetic Algorithm (GA), Simulated Annealing (SA), Solar Tracking, Threshold Acceptance

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References

[1] Roth P, Georgiev A, Boudinov H. Cheap two-axis sun following device, Energy Conversion and Management 2005; 46: 1179–92.

DOI: 10.1016/j.enconman.2004.06.015

Google Scholar

[2] Pitak Khlaichom Kawin Sonthipermpoon, Optimization of solar tracking system based on genetic algorithm, 3rd Conference of the Energy Network of Thailand, (2007).

Google Scholar

[3] Hossein Mousazadeh, Alireza Keyhani, Arzhang Javadi, Hossein Mobli, Karen Abrinia, Ahmad Sharifi, 2009. A review of principle and sun-tracking methods for maximizing solar systems output, Renewable and Sustainable Energy Reviews 13 (2009).

DOI: 10.1016/j.rser.2009.01.022

Google Scholar

[4] Abdallah S, Nijmeh S. Two axes sun tracking system with PLC control. Energy Conversion and Management 2004; 45: 1931–9.

DOI: 10.1016/j.enconman.2003.10.007

Google Scholar

[5] Syamsiah Mashohor, Evaluation of Genetic Algorithm based Solar Tracking System for Photovoltaic Panels; ICSET, (2008).

Google Scholar