شناسایی پارامترهای بارهای الکتریکی با استفاده از ساختار چند متغیره مبتنی بر یادگیری عمیق

محورهای موضوعی : انرژی های تجدیدپذیر

امید ایزدی قهفرخی ¹ (دانشکده مهندسی برق- واحد مرودشت، دانشگاه آزاد اسلامی، مرودشت، ایران)
مزدا معطری ² (مرکز تحقیقات مکاترونیک و هوش مصنوعی- واحد مرودشت، دانشگاه آزاد اسلامی، مرودشت، ایران)
احمد فروزان تبار ³ (دانشکده مهندسی برق- واحد مرودشت، دانشگاه آزاد اسلامی، مرودشت، ایران)

تاریخ دریافت : 1400/05/02 تاریخ پذیرش : 1401/03/24 تاریخ انتشار : 1402/12/01

کلید واژه: مدل‌سازی بار, سیستم اندازه‌گیری گسترده, ساختار یادگیری عمیق چند متغیره, تابع تلفات, شبکه بازگشتی حافظه‌دار,

چکیده مقاله :

مدل‌سازی بار یکی از وظایف ضروری در مطالعات سیستم‌های قدرت محسوب می شوند. با توسعه سیستم‌های قدرت این مسئله بیش از پیش پیچیده تر شده ‌است. روش‌های پیشین مدل‌سازی بار دارای عیوب اساسی مانند الف) حساسیت بالا به نویز، ب) عدم لحاظ همگرایی بارهای الکتریکی در یک شبکه، ج) وابستگی به مدل ریاضی، د) بار محاسباتی بالا و ه) وابستگی به اندازه‌گیری محلی هستند. برای رفع این مشکلات، در این مقاله یک ساختار مبتنی بر یادگیری عمیق توسعه داده شده است که قادر به شناسایی تعداد زیادی از پارامترهای بار به صورت همزمان با سرعت و دقت مطلوب است. ساختار طراحی شده قادر به درک کامل ویژگی‌های زمانی بر مبنای یک ساختار حافظه‌دار بازگشتی است. همچنین، برای تخمین تعداد متغیرهای زیاد یک روش اختصاص‌دهی وزن برای این مدل توسعه داده شده ‌است. نهایتأ، یک تابع تلفات فرمول‌بندی شده ‌است تا مقاوم ‌بودن ساختار در برابر با نویز را افزایش دهد. مطالعات عددی بر روی شبکه 68-شینه IEEE موثر بودن و برتری روش پیشنهادی را در مقایسه با تعدادی از روش‌های کم‌-عمق و عمیق را نشان می دهد.

چکیده انگلیسی:

Electrical load modeling has been considered an essential task in power system studies. With the recent development of power systems, load modeling is becoming more and more challenging. The previous methods on load modeling are suffered from: i) high sensitivity to noise; ii) neglecting the load correlation in a power system, iii) high computational burden, and iv) dependency on the local measurement devices. To address these problems, this paper develops a deep neural network-based structure that can identify a large number of parameters simultaneously with fast performance as well as high accuracy. The designed network can fully understand the temporal features using a gated recurrent neural network-based structure. Furthermore, to provide the ability to estimate a large number of load parameters, a technique to assign the learning weight has been developed. Consequ­ently, to enhance the robustness of the designed network considering noisy conditions, a loss function has been developed in this paper. The numerical results on the IEEE 68-bus system demonstrate the effectiveness and superiority of the proposed network in comparison with several shallow-based and deep-based structures. 

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