Comparison of Meta-Heuristic Optimization Algorithms for Global Maximum Power Point Tracking of Partially Shaded Solar Photovoltaic Systems
[ 1 ] Instytut Elektrotechniki i Elektroniki Przemysłowej, Wydział Automatyki, Robotyki i Elektrotechniki, Politechnika Poznańska | [ P ] employee
[2.2] Automation, electronics, electrical engineering and space technology
2023
scientific article
english
- maximum power point tracking
- PV systems
- cat swarm optimization
- grey wolf optimization
- chimp optimization algorithm
EN Partial shading conditions lead to power mismatches among photovoltaic (PV) panels, re- sulting in the generation of multiple peak power points on the P-V curve. At this point, conventional MPPT algorithms fail to operate effectively. This research work mainly focuses on the exploration of performance optimization and harnessing more power during the partial shading environment of solar PV systems with a single-objective non-linear optimization problem subjected to different operations formulated and solved using recent metaheuristic algorithms such as Cat Swarm Opti- mization (CSO), Grey Wolf Optimization (GWO) and the proposed Chimp Optimization algorithm (ChOA). This research work is implemented on a test system with the help of MATLAB/SIMULINK, and the obtained results are discussed. From the overall results, the metaheuristic methods used by the trackers based on their analysis showed convergence towards the global Maximum Power Point (MPP). Additionally, the proposed ChOA technique shows improved performance over other existing algorithms.
05.08.2023
376-1 - 379-18
Article number: 376
CC BY (attribution alone)
open journal
final published version
05.08.2023
at the time of publication
public
40
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