آمار
| تعداد نشریات | 36 |
| تعداد شماره‌ها | 1,192 |
| تعداد مقالات | 8,579 |
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ارزیابی امنیت دینامیکی سیستم‌‌های قدرت با استفاده از الگوریتم‌های یادگیری جمعی | ||
| علوم و فناوریهای پدافند نوین | ||
| دوره 14، شماره 3 - شماره پیاپی 53، آذر 1402، صفحه 165-178 اصل مقاله (966.55 K) | ||
| نوع مقاله: قدرت- انتقال و توزیع | ||
| نویسندگان | ||
| امین صفری* 1؛ زهرا بهاری لشکری2؛ میثم شهریاری3 | ||
| 1دانشیار، دانشگاه شهید مدنی آذربایجان،تبریز،ایران | ||
| 2دانشجوی کارشناسی ارشد، دانشگاه شهید مدنی آذربایجان،تبریز،ایران | ||
| 3دانشجوی دکتری، دانشگاه شهید مدنی آذربایجان،تبریز،ایران | ||
| تاریخ دریافت: 29 تیر 1402، تاریخ بازنگری: 14 آبان 1402، تاریخ پذیرش: 19 آبان 1402 | ||
| 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 | ||
| ارزیابی امنیت دینامیکی؛ الگو شناسی؛ انتخاب ویژگی؛ پیش‌بینی؛ درخت تصمیم جمعی؛ یادگیری ماشین | ||
| عنوان مقاله [English] | ||
| Dynamic Security Assessment of Power Systems Using Ensemble Learning Algorithms | ||
| نویسندگان [English] | ||
| Amin Safari1؛ Zahra Bahari Lashkari2؛ Meysam Shahriyari3 | ||
| 1Associate Professor, Shahid Madani University of Azerbaijan, Tabriz, Iran | ||
| 2Master's student, Shahid Madani University Azerbaijan, Tabriz, Iran | ||
| 3PhD 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 | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 35 تعداد دریافت فایل اصل مقاله: 27 |
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