Model-based fault detection algorithm for photovoltaic system monitoring

Type
Conference Paper

Authors
Harrou, Fouzi
Sun, Ying
Saidi, Ahmed

KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Statistics Program

KAUST Grant Number
OSR-2015-CRG4-2582

Online Publication Date
2018-02-12

Print Publication Date
2017-11

Date
2018-02-12

Abstract
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.

Acknowledgements
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.

Publisher
Institute of Electrical and Electronics Engineers (IEEE)

Journal
2017 IEEE Symposium Series on Computational Intelligence (SSCI)

Conference/Event Name
2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017

DOI
10.1109/SSCI.2017.8285435

Additional Links
https://ieeexplore.ieee.org/document/8285435/

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