کنترل بار فرکانس در یک سیستم قدرت چند ناحیه‌ای با مشارکت منابع انرژی تجدیدپذیر و خودروی الکتریکی با استفاده از کنترل‌کننده PID مرتبه کسری مبتنی بر شبکه عصبی موجک

محورهای موضوعی : منابع و فن آوری های انرژی های تجدیدپذیر

عباسعلی زمانی ¹ , سید محمد کارگر دهنوی ² , علیرضا رئیسی ³

1 - گروه مهندسی برق- دانشگاه فنی و حرفه‌ای، تهران، ایران
2 - مرکز تحقیقات ریز شبکه‌های هوشمند- واحد نجف‌آباد، دانشگاه آزاد اسلامی، نجف‌آباد، ایران
3 - گروه مهندسی برق- دانشگاه فنی و حرفه‌ای، تهران، ایران

تاریخ دریافت : 1401/04/31 تاریخ پذیرش : 1401/08/21 تاریخ انتشار : 1403/06/01

کلید واژه: توربین بادی, شبکه عصبی موجک, خودرو الکتریکی, کنترل‌کننده PID مرتبه کسری, نیروگاه خورشیدی-حرارتی, کنترل بار فرکانس,

چکیده مقاله :

با تجدید ساختار سیستم قدرت و ادغام منابع انرژی تجدیدپذیر مختلف با رفتار دینامیکی پیچیده و عدم قطعیت‌های عملکردی زیاد، مبحث کنترل بار فرکانس، پیچیدگی‌های بیشتری پیدا کرده است. در این مقاله برای یک سیستم قدرت ترکیبی دو ناحیه‌ای که شامل نیروگاه حرارتی با در نظر گرفتن عوامل غیرخطی مانند باند مرده گاورنر و محدودیت میزان تولید و منابع انرژی تجدیدپذیر شامل توربین بادی، نیروگاه خورشیدی-حرارتی، الکترولایزر، پیل سوختی و خودرو برقی پلاگین است، یک ساختار کنترل بار فرکانس تطبیقی مرتبه کسری، مبتنی بر شبکه‌های عصبی موجک خود بازگشتی و کنترل‌کننده مرتبه کسری با نام کنترل‌کننده تناسبی-انتگرالی-مشتقی (PID) مرتبه کسری مبتنی بر شبکه عصبی موجک (AWNNFOPID) پیشنهاد شده است. برای مقایسه عملکرد کنترل‌کننده AWNNFOPID پیشنهادی چهار سناریو متفاوت در نظر گرفته شده و نتایج با کنترل‌کننده‌های سنتی انتگرال گیر (I)، متناسب-انتگرال گیر (PI)، PID و همچنین با کنترل‌کننده PID مرتبه کسری (FOPID) بهینه مقایسه شده است. نتایج شبیه‌سازی‌ها نشان‌دهنده عملکرد بسیار مناسب کنترل‌کننده AWNNFOPID پیشنهادی بر اساس شاخص‌های عملکردی زمان نشست، زمان صعود، حداکثر فراجهش، حداکثر فروجهش، انتگرال زمانی قدر مطلق خطا (ITAE) و انتگرال قدر مطلق خطا (IAE) در مقایسه با سایر کنترل‌کننده به کار رفته برای سیستم قدرت مورد مطالعه است.

چکیده انگلیسی:

Restructuring of power systems and integration of different renewable energy sources with complex dynamic behaviors and high structural uncertainties has made the issue of load frequency control more important. For a hybrid power system that includes a thermal power plant taking into account nonlinear limitations such as the governor dead band and generator rate constraints and renewable energy sources including a wind turbine, solar-thermal power plant, electrolyzer, fuel cell, and plug-in electric vehicle, this paper proposes an adaptive wavelet neural network fractional order PID controller (AWNNFOPID) based on self-recursive wavelet neural networks and fractional order PID controller. To compare the performance of the proposed AWNNFOPID controller, four different scenarios are considered and the simulation results are compared with traditional I, PI, and PID controllers as well as with the optimized FOPID controller. The simulation results show that the proposed AWNNFOPID controller has better performances than the other control strategies used for the studied hybrid power system based on performance indicators such as settling time, rise time, maximum overshoot, maximum undershoot, integral time absolute error (ITAE), and integral absolute error (IAE).

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