Ensemble Learning Techniques-Based Monitoring Charts for Fault Detection in Photovoltaic Systems

Fouzi Harrou (), Bilal Taghezouit, Sofiane Khadraoui, Abdelkader Dairi, Ying Sun and Amar Hadj Arab
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Fouzi Harrou: Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
Bilal Taghezouit: Centre de Développement des Energies Renouvelables, CDER, B.P. 62, Route de l’Observatoire, Algiers 16340, Algeria
Sofiane Khadraoui: Department of Electrical Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates
Abdelkader Dairi: Laboratoire des Technologies de l’Environnement LTE, BP 1523 Al M’naouar ENP Oran, Oran 31000, Algeria
Ying Sun: Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
Amar Hadj Arab: Centre de Développement des Energies Renouvelables, CDER, B.P. 62, Route de l’Observatoire, Algiers 16340, Algeria

Energies, 2022, vol. 15, issue 18, 1-28

Abstract: Over 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.

Keywords: photovoltaic systems; ensemble bagged trees; anomaly detection; shading; electrical faults; statistical control charts (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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