An Efficient Neural Network-Based Method for Diagnosing Faults of PV Array

Selma Tchoketch Kebir, Nawal Cheggaga, Adrian Ilinca and Sabri Boulouma
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Selma Tchoketch Kebir: Wind Energy Research Laboratory, Université du Québec à Rimouski, 300, Allée des Ursulines, Rimouski, QC G5L 3A1, Canada
Nawal Cheggaga: Laboratory of Electrical Systems and Remote Control, University Saad Dahleb of Blida1, P.O. Box 270 Route de Soumaa, Blida 0900, Algeria
Adrian Ilinca: Wind Energy Research Laboratory, Université du Québec à Rimouski, 300, Allée des Ursulines, Rimouski, QC G5L 3A1, Canada
Sabri Boulouma: Unité de Développement des Equipements Solaires, UDES/Centre de Développement des Energies Renouvelables, CDER, Bou-Ismail, Tipaza 42415, Algeria

Sustainability, 2021, vol. 13, issue 11, 1-27

Abstract: This paper presents an efficient neural network-based method for fault diagnosis in photovoltaic arrays. The proposed method was elaborated on three main steps: the data-feeding step, the fault-modeling step, and the decision step. The first step consists of feeding the real meteorological and electrical data to the neural networks, namely solar irradiance, panel temperature, photovoltaic-current, and photovoltaic-voltage. The second step consists of modeling a healthy mode of operation and five additional faulty operational modes; the modeling process is carried out using two networks of artificial neural networks. From this step, six classes are obtained, where each class corresponds to a predefined model, namely, the faultless scenario and five faulty scenarios. The third step involves the diagnosis decision about the system’s state. Based on the results from the above step, two probabilistic neural networks will classify each generated data according to the six classes. The obtained results show that the developed method can effectively detect different types of faults and classify them. Besides, this method still achieves high performances even in the presence of noises. It provides a diagnosis even in the presence of data injected at reduced real-time, which proves its robustness.

Keywords: photovoltaic array; fault detection; automatic monitoring; diagnosis; artificial intelligence; neural networks; classification (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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