A supervised ensemble learning method for fault diagnosis in photovoltaic strings
Küçük Resim Yok
Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
This study proposes a fault diagnosis method based on the use of a machine learning (ML) technique called ensemble learning (EL) for photovoltaic (PV) systems. EL methods aim to obtain better generalizability and prediction accuracy than a single ML algorithm by combining the predictions of multiple algorithms. In this context, first the most relevant features are selected by using grid-search with cross-validation. Then each learning algorithm and the EL model that will combine them have been improved in terms of parameter optimization. Results show that, with the appropriate features and optimized parameters for each single learning algorithm and the EL model, the proposed method not only improves the classification performance but also has a strong generalization ability for PV system fault diagnosis. © 2021 Elsevier Ltd
Açıklama
Anahtar Kelimeler
Classification, Ensemble learning, Fault diagnosis, Optimization, Photovoltaic monitoring
Kaynak
Energy
WoS Q Değeri
Scopus Q Değeri
Q1
Cilt
227
