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Module Efficiency Improvement Method Based on Gray Wolf Optimization Algorithm in Intelligent Management Environment of Photovoltaic Power Plants.

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Titel: Module Efficiency Improvement Method Based on Gray Wolf Optimization Algorithm in Intelligent Management Environment of Photovoltaic Power Plants.
Autoren: Zhou, Shaoping, Cao, Yikun, Li, Xiang
Quelle: Journal of Combinatorial Mathematics & Combinatorial Computing; Dec2025, Vol. 127b, p5165-5186, 22p
Schlagwörter: PHOTOVOLTAIC power systems, GREY Wolf Optimizer algorithm, SOLAR cell efficiency, MAXIMUM power point trackers, MATHEMATICAL optimization, SOLAR energy, STATISTICAL accuracy
Abstract: With the gradual depletion of fossil energy resources and the increasingly severe environmental problems, photovoltaic power generation as a typical new energy industry has been highly favored in recent years. In the face of the low efficiency of components often faced by photovoltaic power plants in actual operation, this paper proposes a maximum power point tracking algorithm (IGWO) based on the Gray Wolf optimization algorithm, which optimizes and joins the dynamic weights to expand the search range of the algorithm, and improves the efficiency of solar energy utilization. The gray wolf algorithm is further applied to the optimization of photovoltaic (PV) arrays in power stations, and a PV array reconfiguration algorithm based on the gray wolf optimization algorithm is proposed to randomly generate a radial structure by the broken circle method, and the best reconfiguration scheme is obtained through iterative optimization search. The optimization experiment of photovoltaic power station was carried out, and the photovoltaic array reconstruction algorithm in this paper was used to reconstruct in the static shadow occlusion mode, and the GMPP after reconstruction was significantly improved, and the shadow occlusion mode was increased to 14515.565W, 10626.844W, and 10636.467W, respectively, and the tracking accuracy of the IGWO algorithm in this paper also reached 99.9%, 99.5%, and 99.6%, respectively. The tracking accuracy of the IGWO algorithm in this paper for MPPT tracking control is consistently above 99% level under dynamic shadow shading mode. [ABSTRACT FROM AUTHOR]
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Datenbank: Complementary Index
Beschreibung
Abstract:With the gradual depletion of fossil energy resources and the increasingly severe environmental problems, photovoltaic power generation as a typical new energy industry has been highly favored in recent years. In the face of the low efficiency of components often faced by photovoltaic power plants in actual operation, this paper proposes a maximum power point tracking algorithm (IGWO) based on the Gray Wolf optimization algorithm, which optimizes and joins the dynamic weights to expand the search range of the algorithm, and improves the efficiency of solar energy utilization. The gray wolf algorithm is further applied to the optimization of photovoltaic (PV) arrays in power stations, and a PV array reconfiguration algorithm based on the gray wolf optimization algorithm is proposed to randomly generate a radial structure by the broken circle method, and the best reconfiguration scheme is obtained through iterative optimization search. The optimization experiment of photovoltaic power station was carried out, and the photovoltaic array reconstruction algorithm in this paper was used to reconstruct in the static shadow occlusion mode, and the GMPP after reconstruction was significantly improved, and the shadow occlusion mode was increased to 14515.565W, 10626.844W, and 10636.467W, respectively, and the tracking accuracy of the IGWO algorithm in this paper also reached 99.9%, 99.5%, and 99.6%, respectively. The tracking accuracy of the IGWO algorithm in this paper for MPPT tracking control is consistently above 99% level under dynamic shadow shading mode. [ABSTRACT FROM AUTHOR]
ISSN:08353026
DOI:10.61091/jcmcc127b-287