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- W4284698332 abstract "Abstract The developed work in this paper is a part of the detection and identification of faults in systems by modern techniques of artificial intelligence, in a first step we have developed an MLP (Multi- Layer Perceptron ), type neural network to detect shunt faults and shading phenomenon in photovoltaic systems, and in the second part of the work we developed another RNN (recurrent neural networks ) type network in order to identify single and combined faults in PV systems. The results obtained clearly show the performance of the networks developed for the rapid detection of the appearance of faults with the estimation of their times as well as the robust decision to identify the type of faults in the PV system." @default.
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- W4284698332 date "2022-07-05" @default.
- W4284698332 modified "2023-09-26" @default.
- W4284698332 title "Fault diagnosis of a photovoltaic system using recurrent neural networks" @default.
- W4284698332 doi "https://doi.org/10.21203/rs.3.rs-1794474/v1" @default.
- W4284698332 hasPublicationYear "2022" @default.
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