article · Scientific Reports
Accurately determining the electrical parameters of photovoltaic modules is essential for calculating system efficiency and forecasting power generation, yet doing so involves complex non-linear optimisation. This research applies the Artificial Hummingbird Technique, an algorithm inspired by the flight and foraging behaviours of hummingbirds, to identify these missing parameters. The algorithm was evaluated using manufacturer datasheets for three specific modules: STM6-40/36, KC200GT, and PWP 201 polycrystalline units. In comparative assessments against several other modern nature-inspired methods, including the tuna swarm optimiser and the African vultures optimiser, the hummingbird algorithm demonstrated superior performance. Simulation findings confirmed that the approach combines rapid processing speeds and steady convergence with high accuracy, providing a robust computational solution for solar module parameter estimation.
Solar energy systems require precise mathematical models to accurately predict current and power production. Because module datasheets do not supply every internal electrical parameter, reliable extraction techniques are necessary. Using an algorithm with high precision and rapid computation helps engineers design, simulate, and assess photovoltaic installations with greater confidence in their expected performance.
This method could be implemented within solar modelling software and monitoring tools used by photovoltaic design engineers and solar asset operators. Based on the abstract, the research is applied and tested via simulations against manufacturer datasheets for selected commercial modules. Moving towards real-world adoption would likely require integrating the algorithm into existing power system analysis packages and validating it against physical operational data.
AI-generated from the published abstract. Always read the original work before citing.
The parameter extraction of PV models is a nonlinear and multi-model optimization problem. However, it is essential to correctly estimate the parameters of the PV units due to their impact on the PV system efficiency in terms of power and current production. As a result, this study introduces a developed Artificial Hummingbird Technique (AHT) to generate the best values of the ungiven parameters of these PV units. The AHT mimics hummingbirds' unique flying abilities and foraging methods in the wild. The AHT is compared with numerous recent inspired techniques which are tuna swarm optimizer, African vulture's optimizer, teaching learning studying-based optimizer and other recent optimization techniques. The statistical studies and experimental findings show that AHT outperforms other methods in extracting the parameters of various PV models of STM6-40/36, KC200GT and PWP 201 polycrystalline. The AHT's performance is evaluated using the datasheet provided by the manufacturer. To highlight the AHT dominance, its performance is compared to those of other competing techniques. The simulation outcomes demonstrate that the AHT algorithm features a quick processing time and steadily convergence in consort with keeping an elevated level of accuracy in the offered solution.
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DOI: 10.1038/s41598-023-36284-0
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