sustainability Article An Efficient Neural Network-Based Method for Diagnosing Faults of PV Array Selma Tchoketch Kebir 1,2,3, *, Nawal Cheggaga 4 , Adrian Ilinca 1 and Sabri Boulouma 2   Citation: Tchoketch Kebir, S.; Cheggaga, N.; Ilinca, A.; Boulouma, S. An Efficient Neural Network-Based Method for Diagnosing Faults of PV Array. Sustainability 2021, 13, 6194. https://doi.org/10.3390/su13116194 Academic Editor: Catalina Rus-Casas Received: 20 April 2021 Accepted: 25 May 2021 Published: 31 May 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 Wind Energy Research Laboratory, Université du Québec à Rimouski, 300, Allée des Ursulines, Rimouski, QC G5L 3A1, Canada; adrian_ilinca@uqar.ca 2 Unité de Développement des Equipements Solaires, UDES/Centre de Développement des Energies Renouvelables, CDER, Bou-Ismail, Tipaza 42415, Algeria; Sab_blm@yahoo.fr 3 Laboratoire de Dispositifs de Communications et de Conversion Photovoltaique, Ecole Nationale Polytechnique, 10 Avenue Hassen Badi BP 182 El-Harrarach, Algiers 16200, Algeria 4 Laboratory of Electrical Systems and Remote Control, University Saad Dahleb of Blida1, P.O. Box 270 Route de Soumaa, Blida 0900, Algeria; n_chigaga@univ-blida.dz * Correspondence: selma.tchoketch_kebir@g.enp.edu.dz or Selma.TchoketchKebir@uqar.ca 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 1. Introduction In the last few years, there has been a growing interest in developing alternative energies, which are inexhaustible and environment friendly compared to energies derived from fossil deposits (oil, petroleum, and natural gas). Alternate energy encompasses all those renewable resources that do not involve fossil fuels, such as solar, wind, geothermal, hydroelectric, and biomass. Solar energy, both thermal and photovoltaic, shows the greatest growth rate globally. The installed photovoltaic (PV) power increased by over 25% yearly for the last five years. The PV production price dropped significantly during the same period allowing this type of energy to compete freely with alternative sources. With this increased capacity, the fault diagnostic and maintenance of solar PV plants become critical to maintaining the competitiveness of this energy sector [1,2]. The proper diagnosis is crucial to avoid any loss of efficiency, safeguard the system, and guarantee service continuity. The failures detected in a PV system are classified into three categories according to the source of the default (Figure 1): internal, external, and ageing effects [1,3,4]. Sustainability 2021, 13, 6194. https://doi.org/10.3390/su13116194 https://www.mdpi.com/journal/sustainability