طراحی پایدارساز سیستم‌های قدرت با استفاده از کنترل‌کننده تطبیقی مرتبه کسری مبتنی بر شبکه‌های عصبی موجک خودتنظیم

رئیسی, علیرضا; زمانی, عباسعلی

doi:10.48301/kssa.2022.330918.2020

طراحی پایدارساز سیستم‌های قدرت با استفاده از کنترل‌کننده تطبیقی مرتبه کسری مبتنی بر شبکه‌های عصبی موجک خودتنظیم

نوع مقاله : مقاله پژوهشی (کاربردی)

نویسندگان

استادیار، گروه مهندسی برق، دانشگاه فنی و حرفه‌ای، تهران، ایران.

10.48301/kssa.2022.330918.2020

چکیده

اخیراً روش‌های متعددی برای طراحی پایدارساز سیستم قدرت (PSS) ارائه ‌شده است که مبتنی بر کنترل‌کننده‌ها PI، PID و FOPID می‌باشند. در این کنترل‌کننده‌ها درجه آزادی به‌ترتیب از دو به سه و پنج افزایش می‌یابد که منجر به افزایش سرعت همگرایی و گسترش محدوده عملکرد مطلوب کنترل‌کننده نسبت به تغییرات نقطه کار می‌شود اما با افزایش درجه آزادی، تعیین متغیرهای کنترل‌کننده تبدیل به معضل جدیدی شده است چنان‌که تنظیم متغیرهای FOPID دیگر با استفاده از سعی و خطا امکان‌پذیر نیست. یکی از روش‌های مرسوم استفاده از الگوریتم‌های بهینه‌سازی می‌باشد اما باید توجه داشت که سیستم قدرت به‌شدت غیرخطی می‌باشد. در این مقاله الگوریتمی برای طراحی کنترل‌کننده PSS مبتنی بر FOPID پیشنهاد می‌شود که در آن ضرایب کنترل‌کننده براساس شرایط سیستم تنظیم می‌شود. بدین منظور ضرایب کنترل‌کننده براساس گرادیان سیستم قدرت تعریف‌ می‌شوند به‌طوری‌که ضرایب در هرلحظه به روش تطبیقی- گرادیان غیرمستقیم چنان تنظیم می‌شوند که تابع هزینه کنترل‌کننده کمینه شود که نتیجه آن افزایش سرعت میرایی نوسانات می‌باشد. در الگوریتم پیشنهادی برای تخمین گرادیان سیستم قدرت از یک شناساگر مبتنی بر شبکه عصبی موجک خودتنظیم با یادگیری برخط استفاده ‌شده است. در نهایت کنترل‌کننده تطبیقی پیشنهادی برای یک سیستم قدرت دو- ناحیه‌ای، دو- ماشینِ شامل ادوات FACTs از نوع SSSC طراحی شد و عملکرد آن در مقایسه با روش‌های دیگر به‌صورت تحلیلی و عددی ارزیابی شد. نتایج، مؤثر بودن عملکرد روش پیشنهادی در میراسازی نوسانات سیستم قدرت را تأیید می‌کنند.

کلیدواژه‌ها

20.1001.1.23829796.1401.19.3.12.2

موضوعات

قدرت

عنوان مقاله [English]

Power System Stabilizer Design Using Adaptive FOPID Controller based on Self-Learning Wavelet Neural Networks

نویسندگان [English]

Alireza Reisi
Abbas-Ali Zamani

Assistant Professor, Department of Electrical Engineering, Technical and Vocational University (TVU), Tehran, Iran.

چکیده [English]

Several methods were proposed for the design of power system stabilizer, PSS, based on PI, PID, and FOPID controllers. In these controllers, the degree of freedom increases from two to three and five, respectively. Although increasing the degree of freedom can enhance the convergence rate and the robustness of the controller, it does come with more challenges when it comes to tuning the control parameters. For instance, it is no longer possible to adjust FOPID parameters using trial and error. One of the conventional methods is to use optimization algorithms, but it should be noted that the power system is highly non-linear. This research aimed to propose an algorithm to design the PSS controller based on FOPID, in which the controller coefficients were adjusted based on the system conditions. For this purpose, the controller coefficients were defined based on the gradient of the power system, so that the coefficients were adjusted at any moment by the adaptive-indirect gradient method in such a way that the cost function of the controller was minimized, and as a result, the rate of oscillation damping increased. In the proposed algorithm, an identifier based on self-tuning wavelet neural network with online learning was used to estimate the gradient of the power system. Finally, the proposed adaptive controller was designed for a two-zone, two-machine power system including FACTs devices, SSSC-type, and its performance was evaluated in comparison with other methods. The results confirm the effectiveness of the proposed method.

کلیدواژه‌ها [English]

Power system stabilizer
FACTs
Adaptive controller
FOPID
Wavelet neural network

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