کاربرد شبکه‌های عصبی مصنوعی در طراحی یک کنترل‌کننده هوشمند فرکانس برای یک ریزشبکه جزیره‌ای
محورهای موضوعی : مهندسی برق و کامپیوترفرشید حبیبی 1 , حسن بیورانی 2 , جمال مشتاق 3
1 - دانشگاه کردستان
2 - دانشگاه کردستان
3 - دانشگاه کردستان
کلید واژه: تنظیم آنلاین شبکه‌های عصبی مصنوعی ریزشبکه کنترل ثانویه فرکانس,
چکیده مقاله :
افزایش نیاز به انرژی الکتریکی، کمبود سوخت‌های فسیلی و نگرانی‌ها در رابطه با مسایل زیست‌محیطی، سبب ورود هرچه بیشتر منابع جدید از جمله منابع تولید پراکنده و تجدیدپذیر انرژی در سیستم‌های قدرت مدرن شده است. ریزشبکه‌ها به عنوان یکی از جدیدترین مفاهیم در سیستم‌های قدرت از چندین منبع تولید کوچک و بارهای الکتریکی محلی تشکیل شده‌اند. با افزایش تعداد ریزشبکه‌ها بر میزان پیچیدگی و غیر خطی بودن سیستم‌های قدرت افزوده شده و سبب می‌شود که کنترل‌کننده‌های مرسوم و غیر منعطف، کارایی مناسبی را در بازه وسیعی از نقاط کار نشان ندهند. از این رو احتیاج به روش‌های کنترلی هوشمندتر و مناسب‌تر بیش از پیش احساس می‌شود. در این مقاله، شبکه‌های عصبی مصنوعی به عنوان یکی از قوی‌ترین ابزارها در فرایندهای بهینه‌سازی و هوشمندسازی سیستم‌ها به کار گرفته شده است تا ضرایب یک کنترل‌کننده کلاسیک تناسبی- انتگرالی (PI) را به صورت خودکار تنظیم و بهینه نماید. کنترل‌کننده PI، در حلقه ثانویه کنترل فرکانس یک ریزشبکه جزیره‌ایی گمارده شده است. عملکرد مناسب و بهینه روش پیشنهادی در مقایسه با روش‌های کلاسیک در طی شبیه‌سازی‌های مختلف نشان داده می‌شود.
Increasing electrical energy demand, as well as fossil fuel shortages and environmental concerns have caused to use uncommon sources such as distributed generations (DGs) and renewable energy sources (RESs) into modern power systems. A microgrid (MG) system consists of several DGs and RESs which is responsible to provide both electrical and heat powers for local loads. Due to the MGs nonlinearity/complexity which is imposed to the conventional power systems, classical and nonflexible control structures may not represent desirable performance over a wide range of operating conditions. Therefore, more flexible/intelligent control methods are needed most of the past. Hence, in this paper addresses to design an online/self-tuning PI-controller based on artificial neural networks (ANNs) for optimal regulating the MG systems frequency.
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