Smart Drone with Renewable Smart System
Keywords:
Buck-BOOST Converter, Unmanned aerial vehicle, MPPT, BLDC Motor, FLC, Solar PowerAbstract
In order to lessen its negative effects on the environment and to maintain its future operations in a clear, renewable, and sustainable manner, the aviation industry has begun developing designs that are dependent on alternative energy sources but also friendly to the environment and conventional energy. Solar energy has been suggested as a potential remedy. Aerial vehicles driven by solar energy are viewed as essential to limiting the consequences of global warming. In this study, a MATLAB/Simulink environment is used to simulate a mathematical model of a solar-powered BLDC motor of a UAV. under photovoltaic (PV) array systems, the phrase "maximum power point tracking" (MPPT) is crucial to ensuring that, under specific circumstances, the connected systems receive the greatest power output. This study simulates "fuzzy logic control," one of the preferred MPPT methods, using a solar-powered BLDC motor for an unmanned aerial vehicle (UAV) design. The PV cell, MPPT, buck-boost converter, and BLDC motor models in the cascade structure are simulated, tested, and the results are compared to the DC motor technical data. As a result, despite changes in irradiance, the results of mathematical model simulation overlap with motor technical reference values. A mathematical model of a solar-powered BLDC motor for a UAV is created and simulated using the MATLAB/Simulink environment, in contrast to prior solar-powered BLDC motor literature efforts. The fuzzy logic control MPPT technique is preferred for adjusting the maximum power output at the solar cell, and a buck-boost converter structure is connected between the MPPT and the BLDC motor mathematical model. It is recommended for usage in solar-powered UAV designs in the future.
References
. Diab-Marzouk, “SiC-based bidirectional cuk converter with differential power processing and MPPT for a solar powered aircraft,” IEEE Transaction on Transportation Electrification, vol. 1, no. 4, pp.369-381, 2015.
. N. V.P, “Fuzzy logic based hill climbing method for maximum power point tracking in PV system,” in International Conference on Power,Energy and Control (ICPEC), 2013.
. G. Masters, Renewable and Effectife Power System, New Jersey: John Wiley and Sons, 2004.
. A. E. Khateb, “Fuzzy-logic-controller-based SEPIC converter for maximum power point tracking,” IEEE Transactions On Industry Applications, vol. 50, no. 4, pp. 2349-2358, 2014.
. Krismadinata, N. Abd. Rahim, H. Wooi Ping and J. Selvaraj,“Photovoltaic module modeling using imulink/matlab,” in The 3rd International Conference on Sustainable Future for Human Security,2012.
. R, R., &Babu, S. (2013, July). Design and Control of DC-DC Converter using Hybrid Fuzzy PI Controller. IJREAT International Journal of Research in Engineering Technology, 1(3), 1-7.
. Hart, D. W. (2010). Power Electronics (11th ed., pp. 1-477).New York, NY:McGraw-Hil.
. M. E. Sahin, H. I Okumus, "Fuzzy Logic Controlled Synchronous Buck DC-DC Converter for Solar Energy-Hydrogen Systems", INISTA 2009 Conference, 2009.
. M. E. Sahin, "Designing An Electrolyses System With Dc/Dc Buck Converter", M.Sc. Thesis, Gazi University Institute of Science andTechnology, April 2006.
. A. Georgiev, T. Papanchev, and N. Nikolov, “Reliability assessment of power semiconductor devices,” 2016 19th International Symposium on Electrical Apparatus and Technologies (SIELA), pp. 1–4, 2016, doi: 10.1109/SIELA.2016.7543003.
. J. S. Park, K. D. Lee, S. G. Lee, and W. H. Kim, “Unbalanced ZCP compensation method for position sensorless BLDC motor,” IEEE Transactions on Power Electronics, vol. 34, no. 4, pp. 3020–3024, Apr. 2019, doi: 10.1109/TPEL.2018.2868828.
. Y. I. Al Mashhadany, “ANFIS-inverse-controlled PUMA 560 workspace robot with spherical wrist,” Procedia Engineering, vol. 41, pp. 700–709, 2012, doi: 10.1016/j.proeng.2012.07.232.
. P. Electronics, “Comparative study of controller design for four quadrant operation of three,” International Journal of Engineering Sciences & Research Technology, vol. 3, no. 3, pp. 1181-1186, Mar. 2014.
. C. Ganesh, M. Prabhu, M. Rajalakshmi, G. Sumathi, V. Bhola, and S. K. Patnaik, “ANN Based PID Controlled Brushless DC drive System,” ACEEE Int. J. on Electrical and Power Engineering., vol. 03, no. 01, pp. 45–48, 2012, doi: 01.IJEPE.03.01.
. Y. A. Mashhadany, K. S. Gaeid, and M. K. Awsaj, "Intelligent controller for 7-DOF manipulator based upon virtual reality model," 2019 12th International Conference on Developments in eSystems Engineering (DeSE), 2019, pp. 687-692, doi: 10.1109/DeSE.2019.00128.
. K. Sivaraman, R. M. V. Krishnan, B. Sundarraj, and S. Sri Gowthem, “Network failure detection and diagnosis by analyzing syslog and SNS data: Applying big data analysis to network operations,” International Journal of Pure and Applied Mathematics, vol. 8, no. 9, pp. 883–887, 2019, doi: 10.35940/ijitee.I3187.0789S319.
. Y. I. Al-Mashhadany, "Inverse kinematics problem (IKP) of 6-DOF manipulator by locally recurrent neural networks (LRNNs)," 2010 International Conference on Management and Service Science, 2010, pp. 1-5, doi: 10.1109/ICMSS.2010.5577613.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Mays A. Al-bahrany, Ahmad T. AbdulSadda
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who submit papers with this journal agree to the following terms.
