مروری جامع بر روش های داده محور در شبکه های هوشمند برق

محورهای موضوعی : انرژی های تجدیدپذیر

خالق بهروز دهکردی ¹ , هما موحدنژاد ² , مهدی شریفی ³

1 - دانشکده مهندسی کامپیوتر- واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران
2 - دانشکده مهندسی کامپیوتر- واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران
3 - مرکز تحقیقات کلان داده- واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران

تاریخ دریافت : 1400/09/19 تاریخ پذیرش : 1401/02/13 تاریخ انتشار : 1401/02/13

کلید واژه: یادگیری عمیق, شبکه هوشمند برق, یادگیری ماشین, روش‌های داده‌محور, کلان‌داده,

چکیده مقاله :

امروزه شبکه برق به طور چشم گیری در حال تبدیل شدن به شبکه هوشمند (SG)، به عنوان یک چشم انداز امیدوارکننده برای برخورداری از قابلیت اطمینان بالا و مدیریت کارآمد انرژی است. این انتقال به طور پیوسته در حال تغییر است و نیازمند روش های پیشرفته برای پردازش کلان داده های تولید شده از بخش های مختلف است. روش های هوش مصنوعی می توانند از طریق استخراج اطلاعات ارزشمند که توسط دستگاه های اندازه گیری و سنسور های موجود در شبکه تولید می شوند خدمات مبتنی بر داده را ارائه نمایند. به این منظور روش های یادگیری ماشین، یادگیری عمیق، یادگیری تقویتی و یادگیری عمیق تقویتی می توانند به کار گرفته شوند. این روش ها می توانند حجم زیادی از داده های جمع آوری شده را پردازش نموده و راه حل مناسبی برای مشکلات پیچیده صنعت برق ارائه نمایند. از این رو در این مقاله آخرین رویکرد های مبتنی بر هوش مصنوعی مورد استفاده در شبکه هوشمند برق برای کاربرد ها و منابع داده به طور جامع بررسی شده است. همچنین نقش کلان داده در شبکه هوشمند برق و ویژگی های آن از جمله چرخه حیات کلان داده و رویکردهای موثر آن مانند پیشگویی، تعمیرات قابل پیش بینی و تشخیص خطا در صنعت برق بیان می شود.

چکیده انگلیسی:

As a promising vision toward obtaining high reliability and better energy management, nowadays power grid is transferring to the smart grid (SG). This process is changing continuously and needs advanced methods to process big data produced by different segments. Artificial intelligence methods can offer data-driven services by extracting valuable information which is produced by meter devices and sensors in smart grids. To this end, machine learning (ML), deep learning (DL), reinforcement learning (RL), and deep reinforcement learning (DRL) can be applied. These methods are able to process huge amounts of data and propose an appropriate solution to solve power industry complex problems. In this paper, the state-of-the-art approaches based on artificial intelligence used by smart power grids for applications and data sources are investigated. Also, the role of big data in smart power grids, and its features such life cycle, and efficient services such as forecast, predictive maintenance, and fault detection are discussed.

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