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Dergiler / Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi / 2026 / Cilt: 17 Sayı: 2

Photovoltaic Fault Detection Using SE-MobileNet: A Lightweight Channel Attention Network

Hasan Polat(BİNGÖL ÜNİVERSİTESİ, BİNGÖL TEKNİK BİLİMLER MESLEK YÜKSEKOKULU)0000-0001-5535-4832
Oğuz Kaan Polat(BİNGÖL ÜNİVERSİTESİ)0009-0002-2550-8153
Sayfa
—
Yayın Tarihi
2026-07-28
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Furthermore, the model achieves a favorable trade-off between accuracy and computational complexity, with an inference time of 12.73 ms and 78.54 FPS, enabling practical deployment under near real-time conditions in PV monitoring systems.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:The global energy sector is rapidly transitioning from fossil fuels to renewable sources, with solar photovoltaic (PV) systems emerging as a leading solution for sustainable electricity generation.
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Temel Bulgular & Veri:Therefore, early and accurate fault detection is essential to ensure maintained efficiency and system reliability.
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Sonuç & Katkı:Furthermore, the model achieves a favorable trade-off between accuracy and computational complexity, with an inference time of 12.73 ms and 78.54 FPS, enabling practical deployment under near real-time conditions in PV monitoring systems.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Renewable energy · Solar energy · Photovoltaic

Özet

The global energy sector is rapidly transitioning from fossil fuels to renewable sources, with solar photovoltaic (PV) systems emerging as a leading solution for sustainable electricity generation. However, maintaining the cleanliness and structural integrity of PV panels is crucial for optimal operation. Therefore, early and accurate fault detection is essential to ensure maintained efficiency and system reliability. This study introduces a lightweight deep learning framework, termed SE-MobileNet, for diagnosing PV surface faults such as dust and bird droppings, as well as physical and electrical damage. The proposed model incorporates a Squeeze-and-Excitation (SE) block into a MobileNet backbone to enable channel-wise feature recalibration without imposing a significant computational burden. Experimental results demonstrate that this adaptive channel-wise refinement yields a notable improvement in classification performance, increasing the macro F1-score from 0.776 to 0.837, while outperforming a range of benchmark convolutional and transformer-based models. Furthermore, the model achieves a favorable trade-off between accuracy and computational complexity, with an inference time of 12.73 ms and 78.54 FPS, enabling practical deployment under near real-time conditions in PV monitoring systems.

Abstract

The global energy sector is rapidly transitioning from fossil fuels to renewable sources, with solar photovoltaic (PV) systems emerging as a leading solution for sustainable electricity generation. However, maintaining the cleanliness and structural integrity of PV panels is crucial for optimal operation. Therefore, early and accurate fault detection is essential to ensure maintained efficiency and system reliability. This study introduces a lightweight deep learning framework, termed SE-MobileNet, for diagnosing PV surface faults such as dust and bird droppings, as well as physical and electrical damage. The proposed model incorporates a Squeeze-and-Excitation (SE) block into a MobileNet backbone to enable channel-wise feature recalibration without imposing a significant computational burden. Experimental results demonstrate that this adaptive channel-wise refinement yields a notable improvement in classification performance, increasing the macro F1-score from 0.776 to 0.837, while outperforming a range of benchmark convolutional and transformer-based models. Furthermore, the model achieves a favorable trade-off between accuracy and computational complexity, with an inference time of 12.73 ms and 78.54 FPS, enabling practical deployment under near real-time conditions in PV monitoring systems.

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Bu Makaleye Atıf Yap

Polat, H., Polat, O. K. (2026). Photovoltaic Fault Detection Using SE-MobileNet: A Lightweight Channel Attention Network. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, Cilt: 17 Sayı: 2. https://doi.org/10.24012/dumf.1926748

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