One-Class Machine Learning Classifiers-Based Multivariate Feature Extraction for Grid-Connected PV Systems Monitoring under Irradiance Variations

Zahra Yahyaoui, Mansour Hajji, Majdi Mansouri () and Kais Bouzrara
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Zahra Yahyaoui: Research Unit Advanced Materials and Nanotechnologies, Higher Institute of Applied Sciences and Technology of Kasserine, Kairouan University, Kairouan 3100, Tunisia
Mansour Hajji: Research Unit Advanced Materials and Nanotechnologies, Higher Institute of Applied Sciences and Technology of Kasserine, Kairouan University, Kairouan 3100, Tunisia
Majdi Mansouri: Electrical and Computer Engineering Program, Texas A&M University at Qatar, Doha 23874, Qatar
Kais Bouzrara: Laboratory of Automatic Signal and Image Processing, National Engineering School of Monastir, Monastir 5019, Tunisia

Sustainability, 2023, vol. 15, issue 18, 1-20

Abstract: In recent years, photovoltaic (PV) energy production has witnessed overwhelming growth, which has inspired the search for more effective operations. Nevertheless, different PV faults may appear, which leads to various degradation stages. Furthermore, under different irradiance levels, these faults may be misclassified as a healthy mode owing to the high resemblances between them, thus provoking serious challenges in terms of power losses and maintenance costs. Hence, interposing the irradiance variation in grid-connected PV (GCPV) systems modeling is important for monitoring tasks to ensure the effective operation of these systems, to increase their reliability and to prevent false alarms. Therefore, in this paper, a fault detection and diagnosis (FDD) method for the GCPV systems using machine learning (ML) based on principal component analysis (PCA) is proposed in order to ensure the reliability and security of the whole system under irradiance variations. The proposed strategy consists of three main steps: (i) introduce the irradiance variations in PV system modeling because of its great impact on power production; (ii) feature extraction and selection through PCA; and (iii) fault classification using ML techniques. In this study, we generate a database that is used to compare the proposed strategy with the standard strategy (considering a fixed irradiance during FDD), to make, at first, a complete and significant comparative assessment of fault diagnosis and to demonstrate the efficiency of the proposed strategy. The achieved results show the high effectiveness of the proposed one-class classification-based approach to detect and diagnose PV array anomalies, reaching an accuracy up to 99.68%.

Keywords: irradiance variations; grid-connected PV system; feature extraction and selection; fault classification; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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