Aplicação de técnicas de inteligência artificial para identificação de faltas em módulos fotovoltaicos
Photovoltaic solar energy has proven to be a viable alternative that contributes not only to sustainable development but also to ensuring energy supply around the world. The exponential growth of installed capacity in recent years has highlighted the need to ensure the safe operation and reliability...
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| Formato: | doctoralThesis |
| Idioma: | pt_BR |
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Universidade Federal do Rio Grande do Norte
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| Endereço do item: | https://repositorio.ufrn.br/handle/123456789/46008 |
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| Resumo: | Photovoltaic solar energy has proven to be a viable alternative that contributes not only
to sustainable development but also to ensuring energy supply around the world. The
exponential growth of installed capacity in recent years has highlighted the need to ensure the
safe operation and reliability of photovoltaic systems. In this context, the occurrence of faults
in such systems is a crucial issue, as it can significantly impact the generated power, decrease
the modules lifetime, and cause potential risks in the operation. Thus, this research applied
artificial intelligence techniques to detect and diagnose faults in photovoltaic modules. The
faults identified by the proposed methods are short-circuit modules, string disconnection, and
partial shading. Algorithms that detect isolated faults were developed, namely: multilayer
perceptron neural network, probabilistic neural network, and a neuro-fuzzy method, which
combines the use of a neural network with fuzzy logic. All trained algorithms used data
simulated through MATLAB/Simulink® software and tested with experimental data from three
different photovoltaic systems. Two of the studied photovoltaic systems are power plants
installed at the University of Huddersfield, with 2.2 kWp and 4.16 kWp of installed power. The
third photovoltaic system has a maximum power of 5 kWp and is installed at the Federal
Technological University of Paraná. Additionally, training situations in which the dataset was
contaminated by random noise were also considered. The results indicated maximum accuracy
of 99.1% for the lack of short-circuited modules, 100% for string disconnection, and 82.2% for
the lack of partial shading. Furthermore, the analyzes allowed to reaffirm the robustness of the
multi-layer perceptron network for fault detection in photovoltaic systems, even with the
presence of noise in the training data. |
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