Social Grouping Algorithm Aided Maximum Power Point Tracking Scheme for Partial Shaded Photovoltaic Array

Srinivasan Vadivel, Boopathi C. Sengodan, Sridhar Ramasamy, Mominul Ahsan, Julfikar Haider and Eduardo M. G. Rodrigues
Additional contact information
Srinivasan Vadivel: Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Chennai 603 203, India
Boopathi C. Sengodan: Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Chennai 603 203, India
Sridhar Ramasamy: Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Chennai 603 203, India
Mominul Ahsan: Department of Computer Science, University of York, Deramore Lane, York YO10 5GH, UK
Julfikar Haider: Department of Engineering, Manchester Metropolitan University, John Dalton Building, Chester Street, Manchester M1 5GD, UK
Eduardo M. G. Rodrigues: INESC-ID, Sustainable Power Systems Group, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal

Energies, 2022, vol. 15, issue 6, 1-17

Abstract: Photovoltaic (PV) systems-based energy generation is relatively easy to install, even at a large scale, because it is scalable in size and is thus easy to transport. Harnessing maximum power is only possible if maximum power tracking (MPPT) functionality is available as part of the power converter control that interfaces the PV panels to the grid. Solar exposure covering all PV panels is unlikely to happen all the time, which is known as a partial shading (PS) phenomenon. As a result, depending on the MPPT algorithm adopted, it may fail to find a maximum global power peak, being locked into a local power peak. This research work discusses an alternative MPPT control technique inspired in the social group optimization (SGO) algorithm. SGO belongs to the meta-heuristic optimization techniques family. In this sense, the SGO method ability for solving global optimization problems is explored to find the global maximum power point (GMPP) under the presence of local MPPs. The introduced SGO–MPPT was subjected to different PS conditions and complex shading patterns. Then, its performance was compared to other global search MPPT techniques, which include particle swarm optimization (PSO), the dragon fly algorithm (DFO) and the artificial bee colony algorithm (ABC). The simulation outcomes for the SGO–MPPT characterization showed good results, namely rapid global power tracking in less than 0.2 s with reduced oscillation; the efficiency of solar energy harness was slightly above 99%.

Keywords: photovoltaic; partial shading; maximum power point tracking; social group algorithm; soft computing; DC-DC converter (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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