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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-01-01
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| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016823010426 |
| _version_ | 1797347796538359808 |
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| author | Yassine Bouazzi Zahra Yahyaoui Mansour Hajji |
| author_facet | Yassine Bouazzi Zahra Yahyaoui Mansour Hajji |
| author_sort | Yassine Bouazzi |
| collection | DOAJ |
| description | 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) systems. In fact, these uncertain systems are subjected to many failures leading to performance degradation and long downtime maintenance periods. Therefore, fault detection and diagnosis (FDD) are essential to ensure its high dependability. This paper proposes an FDD under climatic conditions variability of uncertain REC systems using deep recurrent neural networks (DRNNs) techniques. Firstly, a novel modeling strategy for REC systems is built. Secondly, different DRNN-based interval-valued data methods are intended to differentiate between the various REC systems operating states. Finally, the hyperparameters of the proposed techniques are tuned using the Bayesian optimization algorithm. The efficiency and robustness of the novel strategy are demonstrated through REC application, using grid-connected photovoltaic (GCPV) systems. The obtained results show the efficiency of the developed strategy by reaching an accuracy rate of 92.40%. |
| first_indexed | 2024-03-08T11:54:03Z |
| format | Article |
| id | doaj.art-1494e5aeae344d5eb7cfe2edc958beec |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-03-08T11:54:03Z |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-1494e5aeae344d5eb7cfe2edc958beec2024-01-24T05:17:06ZengElsevierAlexandria Engineering Journal1110-01682024-01-0186335345Deep recurrent neural networks based Bayesian optimization for fault diagnosis of uncertain GCPV systems depending on outdoor condition variationYassine Bouazzi0Zahra Yahyaoui1Mansour Hajji2Industrial Engineering Department, College of Engineering, University of Ha’il, Ha’il City 81451, Saudi Arabia; Corresponding author.Research Unit Advanced Materials and Nanotechnologies, Higher Institute of Applied Sciences and Technology of Kasserine, Kairouan University, Kasserine 1200, TunisiaResearch Unit Advanced Materials and Nanotechnologies, Higher Institute of Applied Sciences and Technology of Kasserine, Kairouan University, Kasserine 1200, TunisiaEnergy 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) systems. In fact, these uncertain systems are subjected to many failures leading to performance degradation and long downtime maintenance periods. Therefore, fault detection and diagnosis (FDD) are essential to ensure its high dependability. This paper proposes an FDD under climatic conditions variability of uncertain REC systems using deep recurrent neural networks (DRNNs) techniques. Firstly, a novel modeling strategy for REC systems is built. Secondly, different DRNN-based interval-valued data methods are intended to differentiate between the various REC systems operating states. Finally, the hyperparameters of the proposed techniques are tuned using the Bayesian optimization algorithm. The efficiency and robustness of the novel strategy are demonstrated through REC application, using grid-connected photovoltaic (GCPV) systems. The obtained results show the efficiency of the developed strategy by reaching an accuracy rate of 92.40%.http://www.sciencedirect.com/science/article/pii/S1110016823010426Climatic conditions variabilityInterval-valued dataUncertaintiesFault diagnosisDRNNBayesian optimization |
| spellingShingle | Yassine Bouazzi Zahra Yahyaoui Mansour Hajji Deep recurrent neural networks based Bayesian optimization for fault diagnosis of uncertain GCPV systems depending on outdoor condition variation Alexandria Engineering Journal Climatic conditions variability Interval-valued data Uncertainties Fault diagnosis DRNN Bayesian optimization |
| title | Deep recurrent neural networks based Bayesian optimization for fault diagnosis of uncertain GCPV systems depending on outdoor condition variation |
| title_full | Deep recurrent neural networks based Bayesian optimization for fault diagnosis of uncertain GCPV systems depending on outdoor condition variation |
| title_fullStr | Deep recurrent neural networks based Bayesian optimization for fault diagnosis of uncertain GCPV systems depending on outdoor condition variation |
| title_full_unstemmed | Deep recurrent neural networks based Bayesian optimization for fault diagnosis of uncertain GCPV systems depending on outdoor condition variation |
| title_short | Deep recurrent neural networks based Bayesian optimization for fault diagnosis of uncertain GCPV systems depending on outdoor condition variation |
| title_sort | deep recurrent neural networks based bayesian optimization for fault diagnosis of uncertain gcpv systems depending on outdoor condition variation |
| topic | Climatic conditions variability Interval-valued data Uncertainties Fault diagnosis DRNN Bayesian optimization |
| url | http://www.sciencedirect.com/science/article/pii/S1110016823010426 |
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