AIMS Energy, 2020, 8(1): 142-155. doi: 10.3934/energy.2020.1.142

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Design and implementation of battery charging system on solar tracker based stand alone PV using fuzzy modified particle swarm optimization

1 Department of Engineering Physics, Institut Teknologi Sepuluh Nopember, Kampus ITS, Sukolilo, Surabaya, 6011, Indonesia
2 Department of Mathematics, Institut Teknologi Sepuluh Nopember, Kampus ITS, Sukolilo, Surabaya, 6011, Indonesia

Design of battery charging system on solar tracker based PV system and its application has been presented in this paper. To improve the system performance, a solar tracking system as an innovative device of PV has been developed with an intelligent controller. PV equipped by solar tracker can significantly enhace its performance up to 40% of conventional system. In this research solar tracker designed has active tracking mode with double axis. In order to keep the PV performance optimum, a smart battery charging system has been developed and provided to store the electricity generated by PV system. A novel algorithm was implemented to the system which allows the battery charging process to operate quickly and safely. Besides, the components involved in the system are DC-DC converter, sensor, actuator and battery. DC-DC Converter used is Single Ended Primary Inductance Mode (SEPIM) with MOSFET as its actuator. Battery charging system has used intelligent control based on fuzzy-PSO algorithm. In this case, PSO functions to optimize and modify fuzzy parameters to obtain the best model. Optimized fuzzy controller has then been implemented and programmed in an Arduino microcontroller module to generate control signal which commands actuator element to control the voltage of battery through duty cycle manipulation variable. This algorithm has been able to improve the solar charging controller significantly and more convincingly increase PV performance.
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1. Abadi I, Musyafa A, Soeprijanto A (2015) Design and implementation of active two axes solar tracking system using particle swarm optimization based fuzzy logic controller. Int Rev Modell Simul 8: 640-652.

2. Abadi I, Musyafa A, Baskoro KD, et al. (2019) Design and implementation of mobile active Two-Axis solar tracker with reflector based on particle swarm fuzzy controller. Int Rev Modell Simul 12: 113-122.

3. Abadi I, Musyafa A, Soeprijanto A (2014) Design of single axis solar tracking system at photovoltaic panel using fuzzy logic controller. 5th Brunei International Conference on Engineering and Technology (BICET 2014).

4. Abadi I, Musyafa A, Soeprijanto A (2015) Type-2 fuzzy logic controller based PV passive Two-Axis solar tracking system. Int Rev Electr Eng 10: 390-398.

5. Deepthi S, Ponni A, Ranjitha R, et al. (2013) Comparison of efficiencies of Single-Axis tracking system and Dual-Axis tracking system with fixed mount. Int J Eng Sci Innov Technol 2: 425-430.

6. Abadi I, Nur Fitriyanah D, Ul Umam A (2019) Design of maximum power point tracking (MPPT) on two axes solar tracker based on particle swarm fuzzy. AIP Conference Proceedings 2088: 20041.    

7. Cheng P, Peng B, Liu Y, et al. (2015) Optimization of a Fuzzy-Logic-Control-Based MPPT algorithm using the particle swarm optimization technique. Energies 8: 5338-5360.    

8. Falin J (2008) Designing DC/DC converters based on SEPIC topology.

9. Chauhan A (2014) MPPT control PV charging system for lead acid battery mppt control PV charging system for lead acid battery.

10. Padhee S, Pati UC, Mahapatra K (2016) Design of photovoltaic MPPT based charger for lead-acid batteries. 2016 IEEE International Conference on Emerging Technologies and Innovative Business Practices for the Transformation of Societies, EmergiTech, 351-356.

11. Greeshma VJ, Sasidharan R (2016) Battery charging control using fuzzy logic based controller in a photovoltaic system. Int Adv Res J Sci Eng Technol 3: 114-117.    

12. Jamous RA, Seidy E, Tharwat AA, et al. (2015) Modifications of particle swarm optimization techniques and its application on stock market: A survey. Int J Adv Comput Sci Appl 6: 99-108.

13. Yang CH, Hsiao CJ, Chuang LY (2010) Linearly decreasing weight particle swarm optimization with accelerated strategy for data clustering. IAENG Int J Comput Sci 3: 27.

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