Model-based fault detection algorithm for photovoltaic system monitoring
Type
Conference PaperAuthors
Harrou, Fouzi
Sun, Ying

Saidi, Ahmed
KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionStatistics Program
KAUST Grant Number
OSR-2015-CRG4-2582Date
2018-02-12Online Publication Date
2018-02-12Print Publication Date
2017-11Permanent link to this record
http://hdl.handle.net/10754/627857
Metadata
Show full item recordAbstract
Reliable detection of faults in PV systems plays an important role in improving their reliability, productivity, and safety. This paper addresses the detection of faults in the direct current (DC) side of photovoltaic (PV) systems using a statistical approach. Specifically, a simulation model that mimics the theoretical performances of the inspected PV system is designed. Residuals, which are the difference between the measured and estimated output data, are used as a fault indicator. Indeed, residuals are used as the input for the Multivariate CUmulative SUM (MCUSUM) algorithm to detect potential faults. We evaluated the proposed method by using data from an actual 20 MWp grid-connected PV system located in the province of Adrar, Algeria.Citation
Harrou F, Sun Y, Saidi A (2017) Model-based fault detection algorithm for photovoltaic system monitoring. 2017 IEEE Symposium Series on Computational Intelligence (SSCI). Available: http://dx.doi.org/10.1109/SSCI.2017.8285435.Sponsors
This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582.Conference/Event name
2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017Additional Links
https://ieeexplore.ieee.org/document/8285435/ae974a485f413a2113503eed53cd6c53
10.1109/SSCI.2017.8285435