تشخیص و شناسایی خطا در سیستم‌های فتوولتائیک با استفاده از شبکه عصبی عمیق VGG16
محورهای موضوعی : هوش مصنوعی و رباتیک
سمانه عظیمی
1
,
محمد منثوری
2
,
مهدی اخباری
3
1 - گروه قدرت، دانشکده فنی مهندسی، دانشگاه شاهد
2 - دانشگاه شاهد
3 - گروه قدرت، دانشکده فنی مهندسی، دانشگاه شاهد
کلید واژه: آرایه فتوولتائیک, ردیاب نقطه حداکثر توان, طبقه‌بندی خطا, شبکه عصبی کانولوشنی VGG16, اسکالوگرام,
چکیده مقاله :
تشخیص خطا در آرایه های فتوولتائیک (PV) جهت افزایش توان خروجی و همچنین طول عمر مفید یک سیستم PV ضروری است. وجود شرایطی مانند سایه جزئی، خطاهای امپدانس بالا و وجود سامانه ردیاب نقطه حداکثر توان (MPPT)، تشخیص خطا را در شرایط محیطی به چالش می کشد. بیشتر تحقیقات انجام‌شده در این زمینه فقط در چند سناریو از عیوب به شناسایی و طبقه بندی پرداخته اند. این پژوهش با استفاده از شبکه ی عصبی کانولوشنی عمیق از پیش آموزش داده شده VGG16)) و با بهره گیری از ویژگی ها استخراج شده بوسیله اسکالوگرام های دوبعدی تولیدشده از داده های سیستم PV، به شناسایی و طبقه‌بندی خطا در سیستم PV با استفاده از یک شبکه عصبی کاملا متصل می پردازد. برخلاف روش های قبلی پیشنهادشده در ادبیات موضوع تشخیص و طبقه‌بندی عیوب، موارد مختلف معیوب همراه با ترکیب MPPT در مطالعه ما در نظر گرفته‌شده است. در این تحقیق نشان داده‌شده است که روش پیشنهادی شاملCNN از پیش آموزش‌دیده تنظیم‌شده، از روش های موجود بهتر عمل می کند و به‌دقت تشخیص خطای 375/83 درصد دست پیدا می کند.
Fault detection in photovoltaic (PV) arrays is necessary to increase the output power and also the useful life of a PV system. The presence of conditions such as partial shade, high impedance faults, and the maximum power point detector (MPPT) system make the fault detection of PV in environmental conditions more challenging. The literature identified and classified defects just in few scenarios. In this study two-dimensional scalograms are generated from PV system data. The VGG16 as a pretrained convolutional neural network is used for feature extraction. Finally, to identify and classify faults in the PV system a fully connected neural network is trained. Unlike the previous methods proposed in the literature on the subject of defect detection and classification, various defective cases with MPPT combination are considered in this research. It has been shown that the proposed method including pre-trained CNN performs better than the existing methods and achieves an error detection accuracy of 83.375%.
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