Application of Artificial Neural Networks to photovoltaic fault detection and diagnosis: A review

RENEWABLE & SUSTAINABLE ENERGY REVIEWS

卷:138

Review

Li, B.^1,2  Delpha, C.²  Diallo, D.^1,3  Migan-Dubois, A.¹ 

[1] Univ Paris Saclay, Sorbonne Univ, GeePs, Cent Supelec,CNRS, 3 Rue Joliot Curie, F-91192 Gif Sur Yvette, France

[2] Univ Paris Saclay, L2S, Cent Supelec, CNRS, F-91192 Gif Sur Yvette, France

[3] Shanghai Maritime Univ, Shanghai 201306, Peoples R China

关键词: Photovoltaic;    Artificial neural network;    Fault detection;    Fault classification;    Machine learning;    Deep learning;   

DOI  :  10.1016/j.rser.2020.110512

来源: Elsevier

【 摘 要 】

The rapid development of photovoltaic (PV) technology and the growing number and size of PV power plants require increasingly efficient and intelligent health monitoring strategies to ensure reliable operation and high energy availability. Among the various techniques, Artificial Neural Network (ANN) has exhibited the functional capacity to perform the identification and classification of PV faults. In the present review, a systematic study on the application of ANN and hybridized ANN models for PV fault detection and diagnosis (FDD) is conducted. For each application, the targeted PV faults, the detectable faults, the type and amount of data used, the model configuration and the FDD performance are extracted, and analyzed. The main trends, challenges and prospects for the application of ANN for PV FDD are extracted and presented.

【 授权许可】

Free