Collaborative swarm intelligence to estimate PV parameters

•Collaborative swarm intelligence is proposed to estimate PV parameters.•The proposed methodology mitigates premature convergence and population stagnation.•Benchmark functions and experimental data are used to test the new methodology.•The new methodology determines reliable solutions quickly and a...

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Veröffentlicht in:Energy conversion and management Jg. 185; S. 866 - 890
Hauptverfasser: Nunes, H.G.G., Pombo, J.A.N., Bento, P.M.R., Mariano, S.J.P.S., Calado, M.R.A.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Elsevier Ltd 01.04.2019
Elsevier Science Ltd
Schlagworte:
case studies
Circuits
Collaborative swarm intelligence
Computer applications
Data processing
Double-diode model
electronic circuits
Hybrid metaheuristic
Intelligence
Mathematical models
Methodology
Optimization
Parameter estimation
Photovoltaic cells
Photovoltaics
shade
Shading
Single-diode model
Solar cells
Stagnation
Swarm intelligence
system optimization
Double-diode model
Collaborative swarm intelligence
Parameter estimation
Single-diode model
Hybrid metaheuristic
ISSN:0196-8904, 1879-2227
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Zusammenfassung:•Collaborative swarm intelligence is proposed to estimate PV parameters.•The proposed methodology mitigates premature convergence and population stagnation.•Benchmark functions and experimental data are used to test the new methodology.•The new methodology determines reliable solutions quickly and accurately.•Several comparisons and metrics support the obtained results. To properly evaluate, control and optimize photovoltaic (PV) systems, it is crucial to accurately estimate the equivalent electric circuit parameters from the respective mathematical models that characterize the PV cells or modules behavior. This is currently a hot research topic that has attracted the attention of numerous researchers. In this paper, we propose a new hybrid methodology that combines diversification and intensification mechanisms from different metaheuristics (MHs) to estimate PV parameters precisely. The proposed methodology has the capacity to adapt to the specific optimization problem and maintain diversity when building solutions, thus mitigating premature convergence and population stagnation. This methodology can incorporate several MHs (two or more swarms) with different potentialities, enabling a good balance between diversification and intensification mechanisms. Furthermore, it is able to explore a multidimensional search space in different regions simultaneously. To validate its performance, the proposed methodology was compared with other well-established MHs in several benchmark functions, and used to estimate PV parameters in single and double-diode models in two case studies, the first using standard literature data, and the second using measured data from a real application with and without the occurrence of partial shading. The proposed methodology was able to find highly accurate solutions with reduced computational cost and high reliability. Comparisons with the other MHs demonstrate that the proposed methodology presents a very competitive performance when solving the PV parameter estimation problem.
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ISSN:0196-8904
1879-2227
DOI:10.1016/j.enconman.2019.02.003