Enhanced leader particle swarm optimisation (ELPSO): An efficient algorithm for parameter estimation of photovoltaic (PV) cells and modules

•PV cell parameter estimation is considered as a multimodal optimisation problem.•Metaheuristics are likely to converge into local optima.•An improved PSO (ELPSO) is proposed for solving PV cell parameter estimation.•In ELPSO, the leader is enhanced by five successive mutation operators.•Results sho...

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Vydáno v:Solar energy Ročník 159; s. 78 - 87
Hlavní autor: Rezaee Jordehi, A.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Elsevier Ltd 01.01.2018
Pergamon Press Inc
Témata:
Algorithms
Convergence
Electric power
Gallium arsenide
Heuristic
Heuristic methods
Mathematical models
Metaheuristics
Modules
Parameter estimation
Particle swarm optimisation
Particle swarm optimization
Photovoltaic cells
Photovoltaics
PV modeling
Solar cells
Solar energy
Thin films
Parameter estimation
Metaheuristics
Particle swarm optimisation
PV
PV modeling
ISSN:0038-092X, 1471-1257
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Shrnutí:•PV cell parameter estimation is considered as a multimodal optimisation problem.•Metaheuristics are likely to converge into local optima.•An improved PSO (ELPSO) is proposed for solving PV cell parameter estimation.•In ELPSO, the leader is enhanced by five successive mutation operators.•Results show that ELPSO outperforms conventional PSO and some other algorithms. Today, photovoltaic (PV) systems are generating a significant share of electric power. Parameter estimation of photovoltaic cells and modules is a hot research topic and plays an important role in modelling PV systems. This problem is commonly converted into an optimisation problem and is solved by metaheuristic optimisation algorithms. Among metaheuristic optimisation algorithms, particle swarm optimisation (PSO) is a popular leader-based stochastic optimisation algorithm. However, premature convergence is the main drawback of PSO which does not let it to provide high-quality solutions in multimodal problems such as PV cells/modules parameter estimation. In PSO, all particles are pulled toward the leader, so the leader can significantly affect collective performance of the particles. A high-quality leader may pull all particles toward good regions of search space and vice versa. Therefore, in this research, an improved PSO variant, with enhanced leader, named as enhanced leader PSO (ELPSO) is used. In ELPSO, by enhancing the leader through a five-staged successive mutation strategy, the premature convergence problem is mitigated in a way that more accurate circuit model parameters are achieved in the PV cell/module parameter estimation problem. RTC France silicon solar cell, STM6-40/36 module with monocrystalline cells and PVM 752 GaAs thin film cell have been used as the case studies of this research. Parameter estimation results for various PV cells and modules of different technologies confirm that in most of the cases, ELPSO outperforms conventional PSO and a couple of other state of the art optimisation algorithms.
Bibliografie:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ISSN:0038-092X
1471-1257
DOI:10.1016/j.solener.2017.10.063