Fault identification for photovoltaic systems using a multi-output deep learning approach
作者：
Highlights：
• Fault classification and localization are carried out in a photovoltaic system.
• A multi-output scalable machine- and deep-learning approach is developed.
• A 50% reduction in the number of sensors needed per string is achieved.
• Deep Learning models are more resilient to noise and are more accurate.
• Accuracy up to 99.98% and cross-entropy loss as low as 0.002 are attained.
摘要
•Fault classification and localization are carried out in a photovoltaic system.•A multi-output scalable machine- and deep-learning approach is developed.•A 50% reduction in the number of sensors needed per string is achieved.•Deep Learning models are more resilient to noise and are more accurate.•Accuracy up to 99.98% and cross-entropy loss as low as 0.002 are attained.
论文关键词：Photovoltaic faults,Fault classification,Deep learning,Bi-directional long short-term memory,Convolutional neural network
论文评审过程：Received 19 March 2022, Revised 18 July 2022, Accepted 12 August 2022, Available online 17 August 2022, Version of Record 26 August 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.118551
