فصلنامه سیستم های انرژی پایدار

فصلنامه سیستم های انرژی پایدار

تشخیص هوشمند عیوب سطحی پنل‌های خورشیدی با رویکرد بینایی ماشین مبتنی بر یادگیری عمیق

نوع مقاله : مقاله پژوهشی

نویسنده
دانشکدۀ کامپیوتر، دانشکدۀ فنی دانشگاه تهران
10.22059/ses.2026.414893.1244
چکیده
با گسترش استفاده از سامانه‌های فتوولتائیک، پایش سلامت پنل‌های خورشیدی به یکی از موضوعات مهم در بهره‌برداری پایدار، کاهش هزینه‌های نگهداری و افزایش بازده تولید انرژی تبدیل شده است. عیوبی مانند شکستگی، گردوغبار، سایه و تغییرات سطحی می‌توانند باعث افت عملکرد پنل و کاهش عمر مفید آن شوند. روش‌های سنتی بازرسی معمولاً زمان‌بر، وابسته به نیروی انسانی و در مقیاس‌های بزرگ غیرکارآمد هستند. در این پژوهش، نوعی چارچوب مبتنی بر بینایی ماشین و یادگیری عمیق برای تشخیص و مکان‌یابی نواحی سطحی پنل‌های خورشیدی ارائه می‌شود. مدل پیشنهادی از استخراج‌گر ویژگی ResNet101، ماژول استخراج لبه مبتنی بر عملگر سوبل، ماژول توجه کانالی ـ فضایی، ساختار تلفیق ویژگی چندمقیاسی FPN/PAN و سر تشخیص چندمقیاسی تشکیل شده است. خروجی مدل شامل جعبه‌های محدودکننده، کلاس ناحیه و امتیاز اطمینان است و بنابراین، می‌تواند چندین ناحیۀ مختلف را به ‌صورت هم‌زمان در یک تصویر شناسایی کند. مجموعه‌داده مورد استفاده از ترکیب تصاویر گردآوری‌شده از منابع عمومی و داده‌های جمع‌آوری‌شده دستی تشکیل شده و کلاس‌های شکستگی، ناحیۀ سالم، گردوغبار و سایه را شامل می‌شود. نتایج آزمایش‌ها نشان داد مدل پیشنهادی با دستیابی به دقت 954/0، بازخوانی 939/0، امتیاز F1 برابر با 946/0، مقدار mAP@0.5 برابر با 966/0 و مقدار mAP@0.5:0.95 برابر با 766/0، عملکرد بهتری نسبت به مدل‌های پایه و روش‌های جدید تشخیص عیوب فتوولتائیک دارد. همچنین، نتایج مطالعۀ حذف اجزا نشان داد ترکیب اطلاعات لبه‌ای، توجه کانالی ـ فضایی و تلفیق چندمقیاسی نقش مهمی در بهبود دقت تشخیص و مکان‌یابی دارد. بر اساس نتایج، روش پیشنهادی می‌تواند به ‌عنوان یک راهکار کاربردی برای پایش خودکار، پاک‌سازی هدفمند، تعمیر و نگهداری پیش‌بینانۀ پنل‌های خورشیدی مورد استفاده قرار گیرد.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

Intelligent Detection of Surface Defects in Solar Panels Using Deep Learning-Based Computer Vision

نویسنده English

Ali Jelokhani
School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran
چکیده English

With the increasing use of photovoltaic systems, monitoring the health of solar panels has become an important issue for sustainable operation, reducing maintenance costs, and improving energy generation efficiency. Defects such as breakage, dust, shadow, and surface variations can reduce panel performance and shorten its service life. Traditional inspection methods are generally time-consuming, dependent on human labor, and inefficient on a large scale. In this study, a computer vision and deep learning-based framework is proposed for detecting and localizing surface regions in solar panel images. The proposed model consists of a ResNet101 feature extractor, a Sobel operator-based edge extraction module, a spatial–channel attention module, an FPN/PAN multi-scale feature fusion structure, and a multi-scale detection head. The model output includes bounding boxes, region classes, and confidence scores; therefore, it can simultaneously identify multiple different regions in a single image. The dataset used in this study was constructed by combining images collected from public sources with manually collected data and includes four classes: breakage, clean region, dust, and shadow. The experimental results showed that the proposed model, achieving a Precision of 0.954, Recall of 0.939, F1-score of 0.946, mAP@0.5 of 0.966, and mAP@0.5:0.95 of 0.766, outperformed baseline models and recent photovoltaic defect detection methods. Furthermore, the ablation study results showed that combining edge information, spatial–channel attention, and multi-scale feature fusion plays an important role in improving detection and localization accuracy. Based on the results, the proposed method can be used as a practical solution for automatic monitoring, targeted cleaning, and predictive maintenance of solar panels.

کلیدواژه‌ها English

Solar panel
Solar panel defect detection
Computer vision
Deep learning
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