PV-Hess Based Zeta Converter for BLDC Motor Drive using Fuzzy Logic Controller
P. Roja Ramani1, Sheik Mahaboob Shariff2, M. N. V. Venkata Brahmam3
1Pudi Roja Ramani*, Department of EEE, Pragati Engineering College, Surampalem, India.
2Sheik Mahaboob Shariff, Associate Professor, Department of EEE, Pragati Engineering College, Surampalem, India.
3M. N. V. Venkata Brahmam, Associate Professor, Department of EEE, Pragati Engineering College, Surampalem, India.
Manuscript received on December 13, 2019. | Revised Manuscript received on December 22, 2019. | Manuscript published on January 10, 2020. | PP: 1455-1460 | Volume-9 Issue-3, January 2020. | Retrieval Number: B7828129219/2020©BEIESP | DOI: 10.35940/ijitee.B7828.019320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The growing importance of non conventional energy in the auto mobile industry needs the use of brushless DC (BLDC) motor drives the solar photo voltaic (PV). To overcome the disadvantages in the conservative DC-DC converters, Zeta converter is used to optimize power handling through controlling of duty cycle. To mitigate changes in output of PV, the Hybrid Energy Storage System (HESS) is implemented into the PV system to maintain a constant voltage at the BLDC motor input. The PV-HESS system is controlled correctly by a robust power management algorithm. The Zeta converter can meet the smooth performance of the system by using particle swarm optimization technique of maximum power point tracking. By placing set of rules in the FLC controller we get the system stability faster than existed controller. The performance of the fuzzy logic controller built was demonstrated in terms of atmospheric condition changes using MATLAB/ Simulink.
Keywords: Fuzzy logic controller(FLC) Brushless DC (BLDC) motor, Photovoltaic (PV) system, Zeta converter, Particle swarm optimization (PSO).
Scope of the Article: Fuzzy logic