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  3. Vol. 6, No. 4, November 2021
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Vol. 6, No. 4, November 2021

Issue Published : Nov 30, 2021
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Firefly Algorithm For Optimizing Single Axis Solar Tracker

https://doi.org/10.22219/kinetik.v6i4.1338
Oktriza Melfazen
University of Islam Malang
M. Taqijuddin Alawity
Universitas Islam Malang
Denda Dewatama
State Polytechnic of Malang

Corresponding Author(s) : Oktriza Melfazen

oktriza.melfazen@unisma.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 6, No. 4, November 2021
Article Published : Nov 30, 2021

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Abstract

Solar cells mounted on solar panel modules are expected to track sunlight throughout the day to produce maximum energy. The Firefly algorithm (FA) is embedded in the Arduino Mega microcontroller to control the tracking of the sun's position by the solar panel so that the absorption of solar energy can be as much as possible to get maximum electrical energy. The brightest light captured by the solar panel is represented as the light intensity of a firefly. The output of the solar tracking system is obtained by finding the best value of light intensity between fireflies. Parameter changes in FA, such as firefly population, random numbers, and number of iterations affect the results of FA. The largest population, the highest random number and iteration provide the best solution but take a long time to execute. FA can control solar panels in tracking the sun's position precisely with an average error of 1.28% and can absorb a total energy of 666.14 Watt/day. The best solution (98% of setpoint 720) was obtained when the population was set to 50, the random number to 0.8, and iteration to 50. This research can be used as a reference for later using a controller with higher specifications to speed up the FA process time in getting maximum control results.

Keywords

Firefky Algorithm Solar Tracker Motion Single-Axis Energy Absorption
Melfazen, O., Alawity, M. T., & Dewatama, D. (2021). Firefly Algorithm For Optimizing Single Axis Solar Tracker . Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 6(4). https://doi.org/10.22219/kinetik.v6i4.1338
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References
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Read More

References


Rumbayan M, "Mapping of solar energy potential in Indonesia using artificial neural network and geographical information system.," Renewable and Sustainable Energy Reviews, , vol. 16, no. 3, pp. 1437-1449, April 2012. https://doi.org/10.1016/j.rser.2011.11.024

D. E. N. R. Indonesia, "Solar Irradiation Level," Jakarta, 2021, Jakarta,.

D. Minh-Quan, “Potential of Solar Energy In Indonesia,” 58th ICYS 2017, pp. 1-4, 2017.

Adarsh. S, Anand. A, Singla. J, “Increasing the efficiency of a PV system using dual axis solar tracking,” Proceedings of 11th IRF International Conference, pp. 37-41, 2015.

P. Wolfgang, "Solar power for the world. What you wanted to know about photovoltaics.," Journal of Energy in Southern Africa 25(3), vol. 25, pp. 81-86, 2014.

Crobak. P, Skovajsa. J, Zalesak. M,, “Effect of cloudiness on the production of electricity by photovoltaic panels,” MATEC Web of Conferences, vol. 76, pp. 1-4, 2016. https://doi.org/10.1051/matecconf/20167602010

Saharia. BJ, Brahma. H, Sarmah. N,, “A review of algorithms for control and optimization for energy management of hybrid renewable energy systems,” Journal of Renewable and Sustainable Energy, pp. 1-48, 2018. https://doi.org/10.1063/1.5032146

Cahyono, “Ant Colony Optimization Sebagai Tuning PID pada Single Axis Tracking Photovoltaic,” SinarFe7, 2019.

Tubagus. F, “Optimasi Single Axis Tracking untuk Solar Cell menggunakan Bat Algorithm,” SinarFe7, 2019.

Pitons. D, “Pendekatan Algoritma Firefly untuk menyelesaikan masalah pengepakan persegi tiga dimensi,” 2016.

Ali. M, “Optimization on PID and ANFIS Controller on Dual Axis Tracking for Photovoltaic Based on Firefly Algorithm,” ieeexplore, 2019. https://doi.org/10.1109/ICEEIE47180.2019.8981428

Y. Xin-She, “Multiobjective firefly algorithm for continuous optimization,” Springer, 2012. https://doi.org/10.1007/s00366-012-0254-1

Y. Xin-She, "Firefly Algorithm: Recent Advances and Applications," 2013.

Bouziane. K, Dris. K, Boubeker. A, Noureddine. S,, “Optimisation of a Solar Tracker System for Photovoltaic Power Plants in Saharian Region, Example of Ouargla,” egypro, 2014. https://doi.org/10.1016/j.egypro.2014.06.075

Joseph. A, Kamala. J,, “Economic and Backslash Tolerable Solar Tracking System,” International Multi-Conference on Automation, Computing, Communication, Control and Compressed Sensing (iMac4s), pp. 748-753, 2013. https://doi.org/10.1109/IMAC4S.2013.6526506

Tsao. W.C, “Efficiency evaluation of a hybrid miniaturized concentrated photovoltaic for harvesting direct/diffused solar light,” Journal of Optics, 21(3), vol. 21 (3), 2019. https://doi.org/10.1088/2040-8986/aafd7a

S. Wang, “Solving Two-Dimensional HP Model by Firefly Algorithm and Simplified Energy Function.,” Mathematical Problems in Engineering, 2013. https://doi.org/10.1155/2013/398141

Shufat, S. A. A., Kurt, E., & Hancerlioğulları, A., “Modeling and Design of Azimuth-Altitude Dual Axis Solar Tracker for Maximum Solar Energy Generation. International Journal of Renewable Energy Development,” IJRED, vol. 8, pp. 7-13, 2016. https://doi.org/10.14710/ijred.8.1.7-13

Anubhav Taheem, Anish Sachdeva, Vishal S Sharma, “Solar Tracker : A Review.,” Journal of Advanced Research in Alternative Energy, Environment and Ecology, vol. 6, pp. 34-50, 2019. https://doi.org/10.24321/2455.3093.201905

Manomet Current, 2021.

Atmel, “Arduino Mega Datasheet,”

X. S. Yang, “Firefly Algorithm,” dalam Optimization Algorithms. Comput. Optimization, Methods and Algorithms, pp. 13–31, 2011. https://doi.org/10.1007/978-3-642-20859-1

Arora S., Singh S., “The firefly optimization algorithm: convergence analysis and parameter selection,” International Journal of Computer Applications, Vol. %1 dari %2vol. 69, no. 3, p. 48–52, 2013.

Zhang L, Liu L, Yang X-S, Dai Y, “A Novel Hybrid Firefly Algorithm for Global Optimization,” PLoS ONE, vol. 11, 2016. https://doi.org/10.1371/journal.pone.0163230

J. N. Lina, “Firefly Algorithm for Optimazing Problem,” Applied Mechanics and Materials, vol. 421, pp. 512-517, Sedptember 2013. https://doi.org/10.4028/www.scientific.net/AMM.421.512

S. Shoubao. Qingping. L. &. H. L. Yuab S., “A novel wise step strategy for firefly algorithm,” tandfonline, 2014. https://doi.org/10.1080/00207160.2014.907405

M. K. A. Ariyaratne, T. G. I. Fernando, “A Comparative Study on Nature Inspired Algorithms with Firefly Algorithm,” International Journal of Engineering and Technology, vol. 4, 2014.

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KINETIK: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
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