PV panel system modelling method based on neural network

Khaleel, Fadi M.; Hasan, Ibtisam A.; Mohammed, Mohammed J.

doi:10.1063/5.0066820

Cited by 3 publications

(1 citation statement)

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“…In methods based on artificial intelligence, neural networks are usually used to model or identify the solar panel. Neural networks, although in topics such as fault detection [8], production energy prediction [9], estimation of some parameters such as solar cell temperature [10], amount of radiation on cloudy days [11], and losses due to dirt [12] perform well, the error of modeling the characteristics of solar panels is significant compared to other methods [13,14]. In some research [14,15], to improve accuracy, in addition to atmospheric conditions, voltage is also included as input and the output is only current or power.…”

Section: Introductionmentioning

confidence: 99%

Modeling Based on Daily Data of PV Power Plants

Reza Reisi

2024

Advances in Solar Photovoltaic Energy Systems

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Modeling performance characteristics is essential for the design and optimal operation of solar power plants. However, due to the influence of various factors on the performance of solar panels and data changes over time, determining an exact relationship between output power and weather conditions is still challenging. In this chapter, a hybrid method based on genetic programming will be presented for accurate modeling of solar power plant characteristics, which includes two steps. First, three points of open-circuit voltage, maximum power point, and short-circuit current are modeled as functions of atmospheric conditions. For this purpose, by using the modeling process based on genetic programming, relationships with high fit will be obtained for these three points in terms of cell temperature and radiation. Then, with the help of these equations, the voltage–current characteristics are modeled based on the circuit analysis methods and without the need for factory data. To evaluate the modeling for a 3 kW solar power plant, and based on the results, the effectiveness of the proposed method will be shown.

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