PV shading fault detection and classification based on I-V curve using principal component analysis: Application to isolated PV system

• Published in: Solar energy 2019-02, Vol.179, p.1-10
• Main Authors: Fadhel, S., Delpha, C., Diallo, D., Bahri, I., Migan, A., Trabelsi, M., Mimouni, M.F.
• Format: Article
• Language: English
• Subjects: Classification; Current voltage characteristics; Data processing; Electrical measurement; Engineering Sciences; Fault classification; Fault detection; Feature extraction; I-V curves; Photovoltaic cells; Photovoltaics; Power plants; Principal component analysis; Principal components analysis; PV shading faults; Reliability aspects; Shading; Signal and Image processing; Solar cells; Solar energy; Systems health monitoring
• ISSN: 0038-092X; 1471-1257
• DOI: 10.1016/j.solener.2018.12.048

•Shaded PV cells degrade the performance of PV systems.•I-V curves contain useful information for health monitoring.•Data driven method for fault diagnosis can deal with non-controlled solar irradiance.•Principal component analysis has proven its efficacy under real working conditions. Health monitoring and diagnosis of photovoltaic (PV) systems is becoming crucial to maximise the power production, increase the reliability and life service of PV power plants. Operating under faulty conditions, in particular under shading, PV plants have remarkable shape of current-voltage (I-V) characteristics in comparison to reference condition (healthy operation). Based on real electrical measurements (I-V), the present work aims to provide a very simple, robust and low cost Fault Detection and Classification (FDC) method for PV shading faults. At first, we extract the features for different experimental tests under healthy and shading conditions to build the database. The features are then analysed using Principal Component Analysis (PCA). The accuracy of the data classification into the PCA space is evaluated using the confusion matrix as a metric of class separability. The results using experimental data of a 250 Wp PV module are very promising with a successful classification rate higher than 97% with four different configurations. The method is also cost effective as it uses only electrical measurements that are already available. No additional sensors are required.