مدل‌سازی نیروگاه خورشیدی با استفاده از شبکه عصبی مبتنی بر مدار معادل تک‌دیود
محورهای موضوعی : مهندسی برق و کامپیوتر
علی‌رضا رئیسی
1
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روح اله عبداللهی
2
1 - گروه مهندسی برق، دانشگاه ملی مهارت تهران
2 - گروه مهندسی برق، دانشگاه ملی مهارت تهران
کلید واژه: مدل‌سازی, نیروگاه خورشیدی, شبکه عصبی, مدار معادل تک‌دیود,
چکیده مقاله :
روش‌های مختلفی جهت مدل‌سازی پنل‌های خورشیدی ارائه شده است؛ اما مدل‌سازی نیروگاه خورشیدی با استفاده از آنها با چالش‌هایی همراه می‌باشد. مدل‌سازی در روش‌های مبتنی بر مدار معادل، وابسته به داده‌های کارخانه سازنده است که با گذر زمان تغییر می‌کنند. مدل‌سازی مشخصه ولتاژ– جریان با استفاده از روش‌های هوشمند مانند شبکه عصبی به علت دقت پایین مدل‌سازی کمتر مورد توجه قرار گرفت. در این مقاله، روشی مستقل از داده‌های کارخانه سازنده جهت مدل‌سازی نیروگاه خورشیدی ارائه می‌شود؛ چنان که امکان مدل‌سازی دقیق نیروگاه‌های خورشیدی چند سال نصب‌شده نیز فراهم می‌باشد. روش پیشنهادی شامل دو مرحله است؛ در مرحله اول ولتاژ مدار باز، نقطه حداکثر توان و جریان اتصال کوتاه برحسب شرایط جوی با استفاده از شبکه عصبی مدل می‌شوند. در مرحله دوم پارامترهای مجهول مدار معادل توسط روابط تحلیل مداری و با استفاده از خروجی‌های شبکه عصبی تعیین می‌شوند. نهایتاً جهت ارزیابی روش پیشنهادی، مدل‌سازی یک نیروگاه خورشیدی 3 کیلووات انجام شد که نتایج، دقت مناسب روش پیشنهادی جهت مدل‌سازی نیروگاه خورشیدی را نشان می‌دهند.
Various methods have been proposed for modeling solar panels, but modeling solar power plants using them is associated with challenges. In equivalent circuit-based methods, the modeling depends on factory data that changes over time. Modeling of voltage-current characteristic using intelligent methods such as neural network was less considered due to the low accuracy of modeling. In this article, a method independent of the manufacturer's data for modeling the solar power plant is presented, so that it is possible to accurately model the solar power plants that have been installed for several years. The proposed method consists of two steps, in the first step, open circuit voltage, maximum power point and short circuit current are modeled according to atmospheric conditions using neural network. In the second step, the unknown parameters of the equivalent circuit are determined by circuit analysis relations and using neural network outputs. Finally, to evaluate the proposed method, a 3-kW solar power plant was modeled, and the results show the appropriate accuracy of the proposed method for modeling the solar power plant.
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