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dc.contributor.authorHarrou, Fouzi
dc.contributor.authorTaghezouit, Bilal
dc.contributor.authorKhadraoui, Sofiane
dc.contributor.authorDairi, Abdelkader
dc.contributor.authorSun, Ying
dc.contributor.authorHadj Arab, Amar
dc.identifier.citationHarrou, F., Taghezouit, B., Khadraoui, S., Dairi, A., Sun, Y., & Hadj Arab, A. (2022). Ensemble Learning Techniques-Based Monitoring Charts for Fault Detection in Photovoltaic Systems. Energies, 15(18), 6716.
dc.description.abstractOver the past few years, there has been a significant increase in the interest in and adoption of solar energy all over the world. However, despite ongoing efforts to protect photovoltaic (PV) plants, they are continuously exposed to numerous anomalies. If not detected accurately and in a timely manner, anomalies in PV plants may degrade the desired performance and result in severe consequences. Hence, developing effective and flexible methods capable of early detection of anomalies in PV plants is essential for enhancing their management. This paper proposes flexible data-driven techniques to accurately detect anomalies in the DC side of the PV plants. Essentially, this approach amalgamates the desirable characteristics of ensemble learning approaches (i.e., the boosting (BS) and bagging (BG)) and the sensitivity of the Double Exponentially Weighted Moving Average (DEWMA) chart. Here, we employ ensemble learning techniques to exploit their capability to enhance the modeling accuracy and the sensitivity of the DEWMA monitoring chart to uncover potential anomalies. In the ensemble models, the values of parameters are selected with the assistance of the Bayesian optimization algorithm. Here, BS and BG are adopted to obtain residuals, which are then monitored by the DEWMA chart. Kernel density estimation is utilized to define the decision thresholds of the proposed ensemble learning-based charts. The proposed monitoring schemes are illustrated via actual measurements from a 9.54 kW PV plant. Results showed the superior detection performance of the BS and BG-based DEWMA charts with non-parametric threshold in uncovering different types of anomalies, including circuit breaker faults, inverter disconnections, and short-circuit faults. In addition, the performance of the proposed schemes is compared to that of BG and BS-based DEWMA and EWMA charts with parametric thresholds.
dc.description.sponsorshipThis work was supported by funding from the King Abdullah University of Science and Technology (KAUST), Office of Sponsored Research (OSR), under Award No: OSR-2019-CRG7-3800.
dc.publisherMDPI AG
dc.rightsArchived with thanks to Energies under a Creative Commons license, details at:
dc.titleEnsemble Learning Techniques-Based Monitoring Charts for Fault Detection in Photovoltaic Systems
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.contributor.departmentEnvironmental Statistics Group
dc.contributor.departmentStatistics Program
dc.eprint.versionPublisher's Version/PDF
kaust.personHarrou, Fouzi
kaust.personSun, Ying
kaust.acknowledged.supportUnitOffice of Sponsored Research (OSR)

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Archived with thanks to Energies under a Creative Commons license, details at:
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