Abstract
Photovoltaic module parameter estimation is a critical step in observing, analyzing, and optimizing the efficiency of solar power systems. To find the best value for unknown parameters, an efficient optimization strategy is required. This paper presents the implementation of the sooty tern optimization (STO) algorithm for parameter assessment of a solar cell/module. The simulation findings were compared to four pre-existing optimization algorithms: sine cosine (SCA) algorithm, gravitational search algorithm (GSA), hybrid particle swarm optimization and gravitational search algorithm (PSOGSA), and whale optimization (WOA). The convergence rate and root mean square error evaluations show that the STO method surpasses the other studied optimization techniques. Additionally, the statistical results show that the STO method is superior in average resilience and accuracy. The superior performance and reliability of the STO method are further validated by the Friedman ranking test.
Highlights
Publisher’s Note: MDPI stays neutralRecently, clean energy usage has increased significantly as demand for all other fuels declined because of environmental concerns
At least 30 independent runs were performed to metaheuristic algorithms: gravitational search algorithm (GSA) [41], SCA [42], GWO [43], and whale optimization (WOA) [44]
The single-diode model theory was considered for parameter estimation
Summary
Clean energy usage has increased significantly as demand for all other fuels declined because of environmental concerns. Double, and triple diode models of PV cells are widely employed to identify the current-voltage parameters [14,15,16]. The applicability of deterministic algorithms is restricted because of continuity, differentiability, and convexity related to objective functions These algorithms are likewise sensitive to the starting solution and settle at local optima in most cases. The sooty tern optimization (STO) algorithm mimics the attack and migration behavior of sooty terns (birds of tropical oceans) This algorithm provides a good balance between exploration and exploitation strategy and reaches optimal solution without getting trapped in a local solution. These benefits allow researchers to apply the STO for parameter extraction of a solar module.
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