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Title: Random reselection particle swarm optimization for optimal design of solar photovoltaic modules
Authors: Fan, Yi 
Wang, Pengjun 
Heidari, Ali Asghar 
Chen, Huiling 
Turabieh, Hamza 
Mafarja, Majdi 
Keywords: Solar photovoltaic system;Photovoltaic power systems;Solar power plants;Photovoltaic power generation;Swarm intelligence;Particle swarm optimization;Cuckoo search;Single-diode model;Double-diode model
Issue Date: 2022
Publisher: Energy
Abstract: Renewable energy is becoming more popular due to environmental concerns about the previous energy source. Accurate solar photovoltaic system model parameters substantially impact the efficiency of solar energy conversion to electricity. In this matter, swarm and evolutionary optimization algorithms have been widely utilized in dealing with practical problems due to their more straightforward concepts , efficacy, flexibility, and easy to implement procedural framework s . However, the nonlinearity and complexity of the photovoltaic parameter identification caused swarm and evolutionary optimizers to exhibit I mmaturity in the obtained solutions To deal with such concerns on immature convergence and imbalanced searching trends, i n this paper, we proposed the PSOCS algorithm based on the core components of particle swarm optimization ( and the strategy of random reselection of parasitic nests that appeared in the cuckoo search. The paramet ers of the single diode model and the double diode model are identified based on several experiments. Based on the comprehensive comparisons, results indicate that the developed PSOCS algorithm has higher convergence accuracy and better stability than the original PSO , the original cuckoo search and other studied algorithms The findings indicate that we suggest the PSOCS algorithm as an enhanced and efficient approach for dealing with parameter extraction of solar photovoltaic modules. We think this simple variant of PSO can be employed as a tool for the optimal designing of photovoltaic systems
DOI: 10.1016/
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