TY - JOUR
T1 - Fault identification for photovoltaic systems using a multi-output deep learning approach
AU - Mustafa, Zain
AU - Awad, Ahmed S.A.
AU - Azzouz, Maher
AU - Azab, Ahmed
N1 - Funding Information:
This research has received funding from NSERC (Natural Sciences and Engineering Research Council of Canada): Grants # RGPIN 06897-2017 and RGPIN-2017-04990.
Publisher Copyright:
© 2022 Elsevier Ltd
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PY - 2023/1/1
Y1 - 2023/1/1
N2 - Fault classification and localization are imperative to maintaining an efficient photovoltaic (PV) system. Due to the environmental factors that PV systems function in, they can be prone to multiple types of fault conditions such as string-to-string (SS), string-to-ground (SG), and open-circuit faults (OC). If left undetected, these faults can lead to power loss and eventually system failures. To help mitigate such outcomes, this paper presents an approach to detect, classify, and locate SS, SG, and OC faults using multi-output deep learning (DL) algorithms: convolutional neural networks (CNN), long short-term memory (LSTM), and bi-directional long short-term memory (Bi-LSTM) networks. The proposed approach reduces the number of sensors needed per string by 50% compared to the previous approaches in the literature. It also allows for increased scalability and the development of a multi-label dataset through an uncoupled modeling scheme. Fault classification and location are achieved with accuracies of 99.94% and 99.54%, respectively. Further, a comparison is conducted among the proposed DL and conventional machine learning algorithms using multiple evaluation metrics. The proposed method is validated by testing multiple datasets and adding noise. The results obtained reflect high reliability and demonstrate the effectiveness and scalability of the proposed multi-output DL approach.
AB - Fault classification and localization are imperative to maintaining an efficient photovoltaic (PV) system. Due to the environmental factors that PV systems function in, they can be prone to multiple types of fault conditions such as string-to-string (SS), string-to-ground (SG), and open-circuit faults (OC). If left undetected, these faults can lead to power loss and eventually system failures. To help mitigate such outcomes, this paper presents an approach to detect, classify, and locate SS, SG, and OC faults using multi-output deep learning (DL) algorithms: convolutional neural networks (CNN), long short-term memory (LSTM), and bi-directional long short-term memory (Bi-LSTM) networks. The proposed approach reduces the number of sensors needed per string by 50% compared to the previous approaches in the literature. It also allows for increased scalability and the development of a multi-label dataset through an uncoupled modeling scheme. Fault classification and location are achieved with accuracies of 99.94% and 99.54%, respectively. Further, a comparison is conducted among the proposed DL and conventional machine learning algorithms using multiple evaluation metrics. The proposed method is validated by testing multiple datasets and adding noise. The results obtained reflect high reliability and demonstrate the effectiveness and scalability of the proposed multi-output DL approach.
KW - Bi-directional long short-term memory
KW - Convolutional neural network
KW - Deep learning
KW - Fault classification
KW - Photovoltaic faults
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U2 - 10.1016/j.eswa.2022.118551
DO - 10.1016/j.eswa.2022.118551
M3 - Article
AN - SCOPUS:85136477539
SN - 0957-4174
VL - 211
JO - Expert Systems with Applications
JF - Expert Systems with Applications
M1 - 118551
ER -