Deep recurrent neural networks based Bayesian optimization for fault diagnosis of uncertain GCPV systems depending on outdoor condition variation

Deep recurrent neural networks based Bayesian optimization for fault diagnosis of uncertain GCPV systems depending on outdoor condition variation

Energy generated from renewable sources is exposed to extremely dynamic variations in climatic conditions as well as uncertainties (current/voltage variability, noise, measurement errors.). These conditions are relevant issues to be considered when monitoring renewable energy conversion (REC) system...

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Bibliographic Details
Main Authors: Yassine Bouazzi, Zahra Yahyaoui, Mansour Hajji
Format: Article
Language:English
Published: Elsevier 2024-01-01
Series:Alexandria Engineering Journal
Subjects:
Climatic conditions variability
Interval-valued data
Uncertainties
Fault diagnosis
DRNN
Bayesian optimization
Online Access:http://www.sciencedirect.com/science/article/pii/S1110016823010426

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http://www.sciencedirect.com/science/article/pii/S1110016823010426

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