Monitoring of Photovoltaic Systems Using Improved Kernel-Based Learning Schemes
Type
ArticleKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionStatistics Program
KAUST Grant Number
OSR-2019-CRG7-3800Date
2021Submitted Date
2020-12-15Permanent link to this record
http://hdl.handle.net/10754/667699
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Show full item recordAbstract
Data-based procedures for monitoring the operating performance of a PV system are proposed in this article. The only information required to apply the procedures is the availability of system measurements, which are routinely on-line collected via sensors. Here, kernel-based machine learning methods, including support vector regression (SVR) and Gaussian process regression (GPR), are used to model multivariate data from the PV system for fault detection because of their flexibility and capability to nonlinear approximation. Essentially, the SVR and GPR models are adopted to obtain residuals to detect and identify occurred faults. Then, residuals are passed through an exponential smoothing filter to reduce noise and improve data quality. In this work, a monitoring scheme based on kernel density estimation is used to sense faults by examining the generated residuals. Several different scenarios of faults were considered in this study, including PV string fault, partial shading, PV modules short-circuited, module degradation, and line–line faults on the PV array. Using data from a 20 MWp grid-connected PV system, the considered faults were successfully traced using the developed procedures. Also, it has been demonstrated that GPR-based monitoring procedures achieve better detection performance over SVRs to monitor PV systems.Citation
Harrou, F., Saidi, A., Sun, Y., & Khadraoui, S. (2021). Monitoring of Photovoltaic Systems Using Improved Kernel-Based Learning Schemes. IEEE Journal of Photovoltaics, 1–13. doi:10.1109/jphotov.2021.3057169Sponsors
This work was supported by King Abdullah University of Science and Technology (KAUST), Office of Sponsored Research (OSR) under Award OSR-2019-CRG7-3800.Journal
IEEE Journal of PhotovoltaicsAdditional Links
https://ieeexplore.ieee.org/document/9363277/https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9363277
ae974a485f413a2113503eed53cd6c53
10.1109/JPHOTOV.2021.3057169