تشخیص و کلاسه‌بندی برخط خطا در یک مزرعۀ خورشیدی با استفاده از روش بیزین و k نزدیک‌ترین همسایه

نویسندگان

دانشگاه کاشان

چکیده

امروزه منابع تولید پراکنده به‌ویژه سیستم‌های فتوولتائیک به‌عنوان منبع جدید قدرت، درصد بالایی از سرمایه‌گذاری‌ها را به خود اختصاص داده‌اند. تشخیص و تجزیه‌وتحلیل خطا در سیستم‌های فتوولتائیک، یکی از مسائل مهم در بخش بازدهی، ایمنی و قابلیت اطمینان آرایه‌های خورشیدی است. به‌دلیل مشخصۀ غیرخطی خروجی آرایه‌های فتوولتائیک، ماهیت محدودکنندگی جریان، امپدانس خطای بزرگ، شرایط تابش کم، طرح‌های مختلف زمین، شرایط اینورترها و ضعف وسایل حفاظتی، خطاها در آرایه‌ها به‌درستی تشخیص داده نمی‌شوند. بنابراین برای رفع مشکلات حفاظتی، تشخیص خطا استفاده از تکنیک‌های یادگیری ماشین براساس اندازه‌گیری ولتاژ و جریان آرایه و تابش و دما در یک سیستم فتوولتائیک kw6/17 متصل به شبکه پیشنهاد شده است. برای تشخیص نوع و کلاسه‌بندی خطا، انتخاب بهترین روش کلاسه‌بندی با دقت بالا و یافتن ویژگی‌های مناسب در یک آرایۀ فتوولتائیک در مقیاس تجاری، موضوع مهمی است که تاکنون انجام نشده است. داده‌های ورودی برای تشخیص و کلاسه‌بندی خطا با استفاده از روش بیزین و k نزدیک‌ترین همسایه، نتایج شبیه‌سازی به‌ازای مقادیر ورودی دما و تابش‌های مختلف برای کلاس‌های تعریف‌شده‌ای از عیوب خط‌به‌خط و مدارباز با مقادیر مختلف است. نتایج نشان می‌دهد که استفاده از روش‌های پیشنهادی یادگیری ماشین، تشخیص نوع خطا در یک رشته از آرایه بسیار موفقیت‌آمیز است.

کلیدواژه‌ها


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