Model-based fault detection algorithm for photovoltaic system monitoring

Fouzi Harrou; Ying Sun; Ahmed Saidi

doi:10.1109/SSCI.2017.8285435

Model-based fault detection algorithm for photovoltaic system monitoring

Fouzi Harrou, Ying Sun, Ahmed Saidi

Computer, Electrical and Mathematical Sciences and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

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.

Original language	English (US)
Title of host publication	2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-5
Number of pages	5
ISBN (Electronic)	9781538627259
DOIs	https://doi.org/10.1109/SSCI.2017.8285435
State	Published - Jul 1 2017
Event	2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Honolulu, United States Duration: Nov 27 2017 → Dec 1 2017

Publication series

Name	2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
Volume	2018-January

Conference

Conference	2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017
Country/Territory	United States
City	Honolulu
Period	11/27/17 → 12/1/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Control and Optimization

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/SSCI.2017.8285435

Cite this

Harrou, F., Sun, Y., & Saidi, A. (2017). Model-based fault detection algorithm for photovoltaic system monitoring. In 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings (pp. 1-5). (2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI.2017.8285435

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title = "Model-based fault detection algorithm for photovoltaic system monitoring",

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.",

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Harrou, F , Sun, Y & Saidi, A 2017, Model-based fault detection algorithm for photovoltaic system monitoring. in 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings. 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1-5, 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017, Honolulu, United States, 11/27/17. https://doi.org/10.1109/SSCI.2017.8285435

Model-based fault detection algorithm for photovoltaic system monitoring. / Harrou, Fouzi ; Sun, Ying; Saidi, Ahmed.
2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1-5 (2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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Harrou F , Sun Y, Saidi A. Model-based fault detection algorithm for photovoltaic system monitoring. In 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1-5. (2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings). doi: 10.1109/SSCI.2017.8285435

Model-based fault detection algorithm for photovoltaic system monitoring

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus subject areas

UN SDGs

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