Efficient Control of DC Microgrid with Hybrid PV—Fuel Cell and Energy Storage Systems

Subramanian Vasantharaj, Vairavasundaram Indragandhi, Vairavasundaram Subramaniyaswamy, Yuvaraja Teekaraman, Ramya Kuppusamy and Srete Nikolovski
Additional contact information
Subramanian Vasantharaj: School of Electrical Engineering, Vellore Institute of Technology, Vellore City 632014, India
Vairavasundaram Indragandhi: School of Electrical Engineering, Vellore Institute of Technology, Vellore City 632014, India
Vairavasundaram Subramaniyaswamy: School of Computing, SASTRA Deemed University, Thanjavur 613401, India
Yuvaraja Teekaraman: MOBI-Mobility, Logistics and Automotive Technology Research Centre, Vrije Universiteit Brussel, Ixelles, 1050 Brussels, Belgium
Ramya Kuppusamy: Department of Electrical and Electronics Engineering, Sri Sairam College of Engineering, Bangalore City 562106, India
Srete Nikolovski: Power Engineering Department, Faculty of Electrical Engineering, Computer Science and Information Technology, University of Osijek, 31000 Osijek, Croatia

Energies, 2021, vol. 14, issue 11, 1-18

Abstract: Direct current microgrids are attaining attractiveness due to their simpler configuration and high-energy efficiency. Power transmission losses are also reduced since distributed energy resources (DERs) are located near the load. DERs such as solar panels and fuel cells produce the DC supply; hence, the system is more stable and reliable. DC microgrid has a higher power efficiency than AC microgrid. Energy storage systems that are easier to integrate may provide additional benefits. In this paper, the DC micro-grid consists of solar photovoltaic and fuel cell for power generation, proposes a hybrid energy storage system that includes a supercapacitor and lithium–ion battery for the better improvement of power capability in the energy storage system. The main objective of this research work has been done for the enhanced settling point and voltage stability with the help of different maximum power point tracking (MPPT) methods. Different control techniques such as fuzzy logic controller, neural network, and particle swarm optimization are used to evaluate PV and FC through DC–DC boost converters for this enhanced settling point. When the test results are perceived, it is evidently attained that the fuzzy MPPT method provides an increase in the tracking capability of maximum power point and at the same time reduces steady-state oscillations. In addition, the time to capture the maximum power point is 0.035 s. It is about nearly two times faster than neural network controllers and eighteen times faster than for PSO, and it has also been discovered that the preferred approach is faster compared to other control methods.

Keywords: MPPT; fuzzy logic controller (FLC); solar photovoltaic (PV); fuel cell (FC); DC-link; artificial neural network (ANN); particle swarm optimization (PSO) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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