تشخیص وقوع خطای قطع فاز در خطوط انتقال متصل به ریزشبکه¬های مبتنی بر انرژی-های تجدیدپذیر

محورهای موضوعی : مهندسی برق قدرت

حمیدرضا صفا ¹ , علی اصغر قدیمی ²

1 - گروه مهندسی برق، دانشکده مهندسی، دانشگاه اراک، اراک، ایران
2 - گروه مهندسی برق، دانشکده مهندسی، دانشگاه اراک، اراک، ایران

تاریخ دریافت : 1403/02/22 تاریخ پذیرش : 1403/05/18 تاریخ انتشار : 1404/03/30

کلید واژه: خطای قطع فاز, ریزشبکه, انرژی¬های تجدید پذیر, شبکه¬های عصبی مصنوعی,

چکیده مقاله :

اتصال ریزشبکه¬های مبتنی بر انرژی¬های تجدید در خطوط انتقال بیش از پیش افزایش یافته است. حضور این ریزشبکه¬ها در کنار مزایای که دارند اما معضلاتی را از مناظر مختلف بهره برداری، کنترل و حفاظت پیش می¬آورند. اتصال مستقیم این ریزشبکه¬ها به صورت T-off در خطوط انتقال و بدون احداث پست، باعث اختلال شدید در عملکرد الگوریتم¬های حفاظتی خط می¬شود. در این مقاله یک روش تشخیص خطا در خطوط انتقال متصل به ریزشبکه¬های مبتنی بر انرژی¬های تجدید پذیر جهت تشخیص زود هنگام خطای قطع فاز مبتنی بر اطلاعات یک سمت خط (ترمینال ابتدای خط) و با استفاده از روش آموزش یادگیری شبکه¬های عصبی مصنوعی ارائه شده است. شبکه عصبی در نظر گرفته شده در این مقاله ترکیبی از نوع کانولوشنی و بازگشتی با دروازه‌های فراموشی (CNN_LSTM) می¬باشد. مدل ترکیبی شامل یک لایه Conv1D با ۶۴ فیلتر و سایز کرنل ۳، یک لایه MaxPooling1D، دو لایه LSTM با ۳۲ واحد، یک لایه Dropout و یک لایه Dense با یک واحد و فعال‌سازی سیگموئید است. دیتاهای لازم جهت آموزش شبکه عصبی مورد نظر از شبیه سازی شبکه اصلی و پیاده سازی سناریوهای مختلف خطا در سیمولینک نرم افزار متلب استخراج شده¬اند و در نهایت مدل شبکه عصبی مورد نظر در محیط نرم افزار پایتون برنامه نویسی و مدلسازی شده است. طبق نتایج شبیه سازی، دقت نهایی مدل استخراج شده در تشخیص خطای قطع فاز در این توپولوژی پیشنهادی حدود 73/99٪ ارزیابی شده است. نتایج موفقیت آمیز ارائه شده در قسمت نتایج تست و ارزیابی، موید عملکرد مطلوب الگوریتم پیشنهادی در این مقاله می¬باشد.

چکیده انگلیسی:

The connection of renewable energy-based microgrids in transmission lines has significantly increased recently. The presence of REMs, along with the advantages they provide, also leads to problems from different aspects of operation, control, and protection in transmission lines. The direct connection of REMs in the form of T-off in the transmission lines and without the construction of a substation, causes a severe disturbance in the performance of the protection algorithms of the line protection relays. This paper presents a fault detection method in transmission lines connected to REMs for early detection of Broken Conductor Fault (BCF) based on the information of one side of the line (the sending terminal) and using the teaching-learning artificial neural networks (ANNs). The neural network considered in this study is a combination of convolutional neural network and long short-term memory (CNN-LSTM). The hybrid model includes a Conv1D layer with 64 filters and a kernel size of 3, a MaxPooling1D layer, two LSTM layers with 32 units, a Dropout layer and a Dense layer with one unit and sigmoid activation. The necessary data for training the desired ANN have been extracted from the simulation of the main network and the implementation of various fault scenarios in MATLAB/Simulink software, and finally the considered ANN model has been programmed and modeled in the Python software environment. According to the simulation results, the accuracy of the extracted model in detecting the BCF in this proposed topology is estimated to be about 99.73%. The successful results presented in the test and evaluation results section confirm the optimal performance of the proposed algorithm.

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