Open AccessDeep‐learning–based method for faults classification of PV system
Author(s) -
Zaki Sayed A.,
Zhu Honglu,
Fakih Mohammed Al,
Sayed Ahmed Rabee,
Yao Jianxi
Publication year - 2021
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/rpg2.12016
Subject(s) - computer science , photovoltaic system , matlab , fault (geology) , convolutional neural network , artificial intelligence , artificial neural network , fault detection and isolation , feature extraction , heuristic , deep learning , feature (linguistics) , pattern recognition (psychology) , machine learning , engineering , linguistics , philosophy , seismology , geology , electrical engineering , actuator , operating system
Abstract The installation of photovoltaic (PV) system, as a renewable energy source, has significantly increased. Therefore, fast and efficient fault detection and diagnosis technique is highly needed to prevent unpredicted power interruptions. This is obtained in this study in the following steps. First, an efficient meta‐heuristic algorithm is proposed for extracting the optimal five parameters of the PV model in order to assist the MATLAB simulation model. It is used due to its simplicity and high efficiency in building the PV array simulation. Second, a new PV system deep‐learning convolutional neural network (CNN) fault classification method is presented for the advantage of automatic feature extraction, which reduces the computational burden and increases the high classification capability. Finally, for the practical and theoretical validation of the employed CNN model, normal and six fault cases are selected based on different atmospheric conditions. At same time, three electrical indicators are analysed and accordingly chosen as inputs to the proposed classification model. Moreover, the proposed model is compared with other machine‐learning models.