Fault diagnosis method of photovoltaic array based on support vector machine

ABSTRACT

Photovoltaic (PV) arrays are prone to various faults due to the hostile working environment. This paper presents the fault diagnosis algorithm based on support vector machine (SVM) to detect short circuit, open circuit, and lack of irradiation faults that occurred in PV arrays. By analyzing these faults and I–V characteristic curves of PV arrays, the short-circuit current, open-circuit voltage, maximum-power current, and maximum-power voltage are chosen as input parameters of SVM-based fault diagnosis algorithm. The data preprocessing methods are used to improve the quality of fault data set considering the effects of the quality on the performance of SVM-based fault diagnosis algorithm. The grid search and k-fold cross-validation methods are proposed to optimize the parameters of the SVM-based fault diagnosis algorithm. It gets test accuracy of 97% by testing the trained SVM-based fault diagnosis algorithm with 400 data. The experimental results indicate that the SVM-based fault diagnosis algorithm has higher accuracy and generalization ability than other algorithm for fault diagnosis of PV arrays.

KEYWORDS:

• PV arrays
• SVM-based fault diagnosis algorithm
• data preprocessing
• grid search
• k-fold cross-validation

Additional information

Funding

This work was supported by the National Natural Science Foundation of China: [Grant Number 51566015] and the projects of China Qinghai Provincial Science and Technology Department: [Grant Number 2019-HZ-822].

Notes on contributors

Junjie Wang

Junjie Wang, born in 1993, is currently a master candidate at Qinghai University, China. His research interest includes fault diagnosis of PV system. Tel: [phone]; E-mail: [email protected]

Dedong Gao

Dedong Gao, born in 1980, is currently a professor at School of Mechanical Engineering, Qinghai University, China. He received PhD degree from Zhejiang University, China. His research interests include intelligent maintenance of PV system, bio-manufacturing and medical robots. Tel: [phone]; E-mail: [email protected]

Shaokang Zhu

Shaokang Zhu, born in 1998, is currently a master candidate at Qinghai University, China. His current research interest includes fault diagnosis of PV system. Tel: [phone]; E-mail: [email protected]

Shan Wang

Shan Wang, born in 1983, is currently an associated professor at School of Mechanical Engineering, Qinghai University, China. He received master degree from Qinghai University, China. Her research interest includes fault diagnosis of PV system. Tel: [phone]; E-mail: [email protected]

Haixiong Liu

Haixiong Liu, born in 1962, is currently a professor at School of Mechanical Engineering, Qinghai University, China. His research interests include computer aided manufacturing and design. Tel: [phone]; E-mail: [email protected]