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A Novel Hybrid Arithmetic-Based Grey Wolf Optimization Method for Tracking the Global Maximum Power Point of Photovoltaic Systems Under Unequal Irradiance Patterns.
- Source :
-
Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ) . Nov2023, Vol. 48 Issue 11, p15321-15335. 15p. - Publication Year :
- 2023
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Abstract
- Solar power generation (SPG) is uncertain and less efficient under unequal shading conditions. It has a highly nonlinear power versus voltage (P–V) curve that has multiple peaks which are categorized as a global maximum power point (GMPP) and local maximum power point (LMPP). It requires a better tracking method that continuously tracks the GMPP instead of the LMPP. Many conventional methods are unable to track the GMPP. The meta-heuristic methods are the better solutions for this problem. However, still there is a need to improve the performance in terms of tracking speed, tracking efficiency and better exploration and exploitation phases, which are the essential parameters in intelligent optimization. This paper proposed a new hybrid meta-heuristic technique, arithmetic-based grey wolf optimization (AGWO), a combination of arithmetic optimization algorithm (AOA) and grey wolf optimization (GWO). The first one is mainly for the exploration phase, and the other is for the exploitation phase; together, the combination enhances the search behaviour of the algorithm. The proposed AGWO was executed in MATLAB/Simulink and evaluated with a real-time simulator to validate its performance. This is compared with other relevant methods like AOA, GWO, particle swarm optimization (PSO) and perturb and observe (P&O) methods. The results indicated that the suggested AGWO technique shows better improvement in tracking efficiency of about 16.41% more than P&O during pattern 1. Also, it shows an excellent tracking speed as compared with PSO is 60.42% raise during pattern 3. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 2193567X
- Volume :
- 48
- Issue :
- 11
- Database :
- Academic Search Index
- Journal :
- Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. )
- Publication Type :
- Academic Journal
- Accession number :
- 172443201
- Full Text :
- https://doi.org/10.1007/s13369-023-08006-1