تشخیص و شناسایی خطا در سیستم‌های فتوولتائیک با استفاده از شبکه عصبی عمیق VGG16
الموضوعات :
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
الکلمات المفتاحية: آرایه فتوولتائیک, ردیاب نقطه حداکثر توان, طبقه‌بندی خطا, شبکه عصبی کانولوشنی VGG16, اسکالوگرام,
ملخص المقالة :
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.
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