Fault Detection and Diagnosis of a Photovoltaic System Based on Deep Learning Using the Combination of a Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU)

Ahmed Faris Amiri (), Sofiane Kichou (), Houcine Oudira, Aissa Chouder and Santiago Silvestre
Additional contact information
Ahmed Faris Amiri: Laboratory of Electrical Engineering (LGE), Electronic Department, University of M’sila, P.O. Box 166 Ichebilia, M’sila 28000, Algeria
Sofiane Kichou: Czech Technical University in Prague, University Centre for Energy Efficient Buildings, 1024 Třinecká St., 27343 Buštěhrad, Czech Republic
Houcine Oudira: Laboratory of Electrical Engineering (LGE), Electronic Department, University of M’sila, P.O. Box 166 Ichebilia, M’sila 28000, Algeria
Aissa Chouder: Laboratory of Electrical Engineering (LGE), Electrical Engineering Department, University of M’sila, P.O. Box 166 Ichebilia, M’sila 28000, Algeria
Santiago Silvestre: Department of Electronic Engineering, Universitat Politècnica de Catalunya (UPC), Mòdul C5 Campus Nord UPC, Jordi Girona 1-3, 08034 Barcelona, Spain

Sustainability, 2024, vol. 16, issue 3, 1-21

Abstract: The meticulous monitoring and diagnosis of faults in photovoltaic (PV) systems enhances their reliability and facilitates a smooth transition to sustainable energy. This paper introduces a novel application of deep learning for fault detection and diagnosis in PV systems, employing a three-step approach. Firstly, a robust PV model is developed and fine-tuned using a heuristic optimization approach. Secondly, a comprehensive database is constructed, incorporating PV model data alongside monitored module temperature and solar irradiance for both healthy and faulty operation conditions. Lastly, fault classification utilizes features extracted from a combination consisting of a Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU). The amalgamation of parallel and sequential processing enables the neural network to leverage the strengths of both convolutional and recurrent layers concurrently, facilitating effective fault detection and diagnosis. The results affirm the proposed technique’s efficacy in detecting and classifying various PV fault types, such as open circuits, short circuits, and partial shading. Furthermore, this work underscores the significance of dividing fault detection and diagnosis into two distinct steps rather than employing deep learning neural networks to determine fault types directly.

Keywords: photovoltaic (PV) system; fault detection; fault classification; deep learning; Convolutional Neural Network (CNN); Bidirectional Gated Recurrent Unit (Bi-GRU); PV modeling (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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