Deep learning approaches for visual faults diagnosis of photovoltaic systems: State-of-the-Art review

• Published in: Results in engineering Vol. 23; p. 102622
• Main Authors: Jalal, Marium, Khalil, Ihsan Ullah, Haq, Azhar ul
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2024; Elsevier
• Subjects: Deep learning; Fault classification; Machine learning; PV visual faults; Deep learning; Fault classification; PV visual faults; Machine learning
• ISSN: 2590-1230; 2590-1230
• DOI: 10.1016/j.rineng.2024.102622

PV systems are prone to external environmental conditions that affect PV system operations. Visual inspection of the impacts of faults on PV system is considered a better practice rather than onsite fault detection mechanisms. Faults such as hotspot, dark area, cracks, glass break, wavy lines, snail tracks, corrosion, discoloration, junction box failure and delamination faults have different visual symptoms. EL technology, infrared thermography, and photoluminescence approaches are used to extract and visualize the impact of faults on PV modules. DL based algorithms such as, CNN, ANN, RNN, AE, DBN, TL and hybrid algorithms have shown promising results in domain of visual PV fault detection. This article critically overviews working mechanism of DL algorithms in terms of their limitations, complexity, interpretability, training dataset requirements and capability to work with another DL algorithms. This research article also reviews, critically analyzes, and systematically presents different clustering algorithms based on their clustering mechanism, distance metrics, convergence criteria. Additionally, their performance is also evaluated in terms of DI, CHI, DBI, S-score, and homogeneity. Moreover, this research work explicitly identifies and explains the limitations and contributions of recent and older techniques employed for features extraction, data preprocessing, and decision making by performing SWOT analysis. This research work also recommends future research directions for industry and academia. •PV systems are affected by environmental conditions, making visual inspection of faults easy.•Electroluminescence (EL), infrared thermography (IRT), and photoluminescence (PL) technologies are used to visualize faults.•DL algorithms have shown promising results in visual PV fault detection.•This article highlights limitations and contributions of DL algorithms in feature extraction and decision-making.•Future research directions are recommended for both industry and academia to advance PV fault detection methods.