A simple and effective detection strategy using double exponential scheme for photovoltaic systems monitoring
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
KAUST Grant NumberOSR-2019-CRG7-3800
Embargo End Date2022-12-23
Permanent link to this recordhttp://hdl.handle.net/10754/666746
MetadataShow full item record
AbstractEffective and efficient monitoring of a photovoltaic plant are indispensable to maintain the generated power at the desired specifications. In this work, a simple and effective monitoring method based on parametric models and the double exponentially smoothing scheme is designed to monitor photovoltaic systems. This method merges the simplicity and flexibility of empirical models and the sensitivity of the double exponentially smoothing strategy to uncover small deviations. Essentially, the empirical models are adopted to obtain residuals to detect and identify occurred faults. Here, a double exponentially smoothing scheme is used to sense faults by examining the generated residuals. Moreover, to extend the flexibility of the double exponentially smoothing approach, a nonparametric detection threshold has been computed via kernel density estimation. Several different scenarios of faults were considered to assess the developed method, including PV string fault, inverter disconnection, circuit breaker faults, partial shading, PV modules short-circuited, and soiling on the PV array. It is showed using real data from a 9.54 kWp photovoltaic system that the considered faults were successfully traced using the developed approach.
CitationTaghezouit, B., Harrou, F., Sun, Y., Arab, A. H., & Larbes, C. (2021). A simple and effective detection strategy using double exponential scheme for photovoltaic systems monitoring. Solar Energy, 214, 337–354. doi:10.1016/j.solener.2020.10.086
SponsorsThis publication is validated by experimental data produced within the photovoltaic solar energy division of the Centre de Développement des Energies Renouvelables (CDER). This publication is based upon work supported by King Abdullah University of Science and Technology (KAUST), Office of Sponsored Research (OSR) under Award No: OSR-2019-CRG7-3800.