TY - GEN
T1 - Design of MPPT based fuzzy logic for solar-powered unmanned aerial vehicle application
AU - Suryoatmojo, H.
AU - Mardiyanto, R.
AU - Riawan, D. C.
AU - Setijadi, E.
AU - Anam, S.
AU - Azmi, F. A.
AU - Putra, Y. D.
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/8/13
Y1 - 2018/8/13
N2 - By converting sun energy into electrical energy, solar cells are a technology of renewable energy that is abundant and low emissions. Meanwhile, solar cells can reduce conventional vehicle exhaust emissions by 92%. Therefore, solar panels have a great chance to be applied to a vehicle such as Unmanned Aerial Vehicle (UAV). Unfortunately, solar panel is a non-linear energy source whose output power changes depending on the irradiance and ambient temperature. If operated under normal circumstances, solar panels will not absorb the solar power optimally due to the characteristic curves of the solar panels. In order maximize the power from solar panels, it needs Maximum Power Point Tracking (MPPT), which is a power optimization method by conditioning the output voltage of the solar panel. On UAV, the sun's irradiance is changing very rapidly, so the conventional MPPT is less efficient to use, because it has a slow response and has oscillations when the power is in maximum. In this research, it has been designed and implemented of MPPT system using fuzzy logic control. Fuzzy logic control can speed up the system response towards the load changes, and reduce the oscillations that occur at maximum power as well. A DC-DC converter, as the actuator of the voltage conditioner, should be selected which has as light as possible in weight so that it will not increase the workload of the aircraft. Simulation and experiment have been done in this research. It shows that MPPT using Fuzzy Logic can reduce the power losses up to 4.5%.
AB - By converting sun energy into electrical energy, solar cells are a technology of renewable energy that is abundant and low emissions. Meanwhile, solar cells can reduce conventional vehicle exhaust emissions by 92%. Therefore, solar panels have a great chance to be applied to a vehicle such as Unmanned Aerial Vehicle (UAV). Unfortunately, solar panel is a non-linear energy source whose output power changes depending on the irradiance and ambient temperature. If operated under normal circumstances, solar panels will not absorb the solar power optimally due to the characteristic curves of the solar panels. In order maximize the power from solar panels, it needs Maximum Power Point Tracking (MPPT), which is a power optimization method by conditioning the output voltage of the solar panel. On UAV, the sun's irradiance is changing very rapidly, so the conventional MPPT is less efficient to use, because it has a slow response and has oscillations when the power is in maximum. In this research, it has been designed and implemented of MPPT system using fuzzy logic control. Fuzzy logic control can speed up the system response towards the load changes, and reduce the oscillations that occur at maximum power as well. A DC-DC converter, as the actuator of the voltage conditioner, should be selected which has as light as possible in weight so that it will not increase the workload of the aircraft. Simulation and experiment have been done in this research. It shows that MPPT using Fuzzy Logic can reduce the power losses up to 4.5%.
KW - Fuzzy Logic
KW - Hill Climbing
KW - MPPT
KW - Solar Powered Plane Unmanned
UR - http://www.scopus.com/inward/record.url?scp=85053155828&partnerID=8YFLogxK
U2 - 10.1109/ICEAST.2018.8434430
DO - 10.1109/ICEAST.2018.8434430
M3 - Conference contribution
AN - SCOPUS:85053155828
SN - 9781538649565
T3 - ICEAST 2018 - 4th International Conference on Engineering, Applied Sciences and Technology: Exploring Innovative Solutions for Smart Society
BT - ICEAST 2018 - 4th International Conference on Engineering, Applied Sciences and Technology
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Conference on Engineering, Applied Sciences and Technology, ICEAST 2018
Y2 - 4 July 2018 through 7 July 2018
ER -