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Advisor(s)
Abstract(s)
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 wellestablished
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.
Description
Keywords
Collaborative swarm intelligence Hybrid metaheuristic Parameter estimation Single-diode model Double-diode model