Photovoltaic system fault detection techniques: a review

被引:22
|
作者
El-Banby, Ghada M. [1 ]
Moawad, Nada M. [2 ]
Abouzalm, Belal A. [1 ]
Abouzaid, Wessam F. [2 ]
Ramadan, E. A. [1 ]
机构
[1] Menoufia Univ, Fac Elect Engn, Dept Ind Elect & Control Engn, Menoufia 32952, Egypt
[2] Kafrelsheikh Univ, Fac Engn, Dept Elect Engn Comp & Control Syst, Kafrelsheikh 35516, Egypt
来源
NEURAL COMPUTING & APPLICATIONS | 2023年 / 35卷 / 35期
关键词
Photovoltaic (PV) systems; PV failures; Fault detection system; Artificial intelligence; CLASSIFICATION; DIAGNOSIS; THERMOGRAPHY;
D O I
10.1007/s00521-023-09041-7
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Solar energy has received great interest in recent years, for electric power generation. Furthermore, photovoltaic (PV) systems have been widely spread over the world because of the technological advances in this field. However, these PV systems need accurate monitoring and periodic follow-up in order to achieve and optimize their performance. The PV systems are influenced by various types of faults, ranging from temporary to permanent failures. A PV system failure poses a significant challenge in determining the type and location of faults to quickly and cost-effectively maintain the required performance of the system without disturbing its normal operation. Therefore, a suitable fault detection system should be enabled to minimize the damage caused by the faulty PV module and protect the PV system from various losses. In this work, different classifications of PV faults and fault detection techniques are presented. Specifically, thermography methods and their benefits in classifying and localizing different types of faults are addressed. In addition, an overview of recent techniques using different artificial intelligence tools with thermography methods is also presented.
引用
收藏
页码:24829 / 24842
页数:14
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