ارزیابی امنیت دینامیکی سیستم‌‌های قدرت با استفاده از الگوریتم‌های یادگیری جمعی

صفری, امین; بهاری لشکری, زهرا; شهریاری, میثم

ارزیابی امنیت دینامیکی سیستم‌‌های قدرت با استفاده از الگوریتم‌های یادگیری جمعی

نوع مقاله : قدرت- انتقال و توزیع

نویسندگان

¹ دانشیار، دانشگاه شهید مدنی آذربایجان،تبریز،ایران

² دانشجوی کارشناسی ارشد، دانشگاه شهید مدنی آذربایجان،تبریز،ایران

³ دانشجوی دکتری، دانشگاه شهید مدنی آذربایجان،تبریز،ایران

چکیده

باتوجه‌به اهمیت ارزیابی امنیت دینامیکی و ضرورت انجام اقدامات کنترلی پس از بروز اغتشاش، ارزیابی امنیت دینامیکی آنلاین جایگزین ارزیابی آفلاین شده است. روشهایی که به‌طور رایج در ارزیابی امنیت دینامیکی به کار برده میشوند در مواجهه با پیشامدهای خطرناک که سرعت وقوع بالایی داشته باشند، مناسب نیستند. بنابراین، ضروری است تا ارزیابی پایداری گذرا به‌صورت آنلاین و در زمان واقعی انجام گیرد تا اپراتورها فرصت مناسب برای انجام اقدام اصلاحی را داشته باشند. به همین منظور در این مطالعه، از روش‌های یادگیری ماشین جمعی جهت ارزیابی امنیت دینامیکی آنلاین بهره گرفته شده است. مسئله موردبررسی یک طبقهبندی چند کلاسی است که به طبقهبندی وضعیت امنیت دینامیکی سیستم میپردازد. روش پیشنهادی بر روی دو سیستم استاندارد مورد ارزیابی قرارگرفته است. همچنین مقایسهای بین روشهای یادگیری جمعی پیشنهادی با الگوریتم‌های منفرد انجام‌گرفته است. نتایج حاکی از آن است که روش پیشنهادی ضمن دقت، از عملکرد مناسبی برخوردار بوده و برای ارزیابی امنیت دینامیکی آنلاین مناسب بوده است.

کلیدواژه‌ها

20.1001.1.26762935.1402.14.3.3.7

موضوعات

قدرت-سیستم قدرت

عنوان مقاله [English]

Dynamic Security Assessment of Power Systems Using Ensemble Learning Algorithms

نویسندگان [English]

Amin Safari ¹
Zahra Bahari Lashkari ²
Meysam Shahriyari ³

¹ Associate Professor, Shahid Madani University of Azerbaijan, Tabriz, Iran

² Master's student, Shahid Madani University Azerbaijan, Tabriz, Iran

³ PhD student, Shahid Madani University of Azerbaijan, Tabriz, Iran

چکیده [English]

Considering the importance of dynamic security assessment and the necessity of implementing control measures after a disturbance, online dynamic security assessment has replaced offline assessment. Methods which are commonly used in dynamic security evaluation are not suitable for serious events which have a high rate of occurrence. Therefore, it is essential to perform real time transient stability assessment in order to increase operators opportunity to take remedial actions. For this purpose, in this study, ensemble machine learning methods have been used to evaluate online dynamic security. The investigated problem is a multi-class classification that deals with classifying of system’s dynamic security status. The proposed method has been evaluated on two standard systems. Also, a comparison has been made between the proposed ensemble learning methods and individual algorithms. The results indicate that the proposed method, not only accurate but also has good performance in evaluating online dynamic security.

کلیدواژه‌ها [English]

Dynamic Security zAssessment
Pattern Recognition
Feature Selection
Prediction
Ensemble Decision Tree
Machine Learning

https://creativecommons.org/licenses/by/4.0/

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