ساختار توزیع شده برای کنترل توان راکتیو
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ساختار توزیع شده برای کنترل توان راکتیو

عنوان فارسی مقاله: یک ساختار جدید دو مرحله ای توزیع شده برای کنترل توان راکتیو
عنوان انگلیسی مقاله: A novel two-stage distributed structure for reactive power control
مجله/کنفرانس: علوم مهندسی و فناوری، یک مجله بین المللی - Engineering Science And Technology, An International Journal
رشته های تحصیلی مرتبط: برق
گرایش های تحصیلی مرتبط: توزیع و انتقال، مهندسی کنترل، مهندسی الکترونیک، الکترونیک قدرت
کلمات کلیدی فارسی: توزیع بهینه توان راکتیو (ORPD)، ساختار كنترل توزیع شده، رویکرد تقسيم پذیری، منابع انرژي توزيع شده (DER)
کلمات کلیدی انگلیسی: Optimal Reactive Power Dispatch (ORPD)، Distributed control structure، Partitioning approach، Distributed Energy Resources (DER)
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journals List - DOAJ
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.jestch.2019.03.003
دانشگاه: Shahrood University of Technology, Shahrood, Iran
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 4/425 در سال 2018
شاخص H_index: 29 در سال 2019
شاخص SJR: 0/765 در سال 2018
شناسه ISSN: 2215-0986
شاخص Quartile (چارک): Q1 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 21
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: دارد
کد محصول: E12951
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


1- Introduction


2- Proposed structure


3- Simulation results


4- Conclusion


References

نمونه متن انگلیسی مقاله

Abstract


In this paper, a new two-stage approach has been presented for the reactive power control of power systems. In the first stage, the transmission network is divided into several parts using a partitioning approach based on graph concept. In the second stage, a hierarchical distributed framework based on a System of Systems (SoS) concept has been proposed for optimal reactive power dispatch. In this structure, every section of the grid is controlled by a smart agent. Agents are interconnected and exchange the required data via a telecommunication network. In this paper, the amounts of active and reactive power exchanged between agents are considered as boundary and common parameters. Our proposed method is implemented on the IEEE 118-bus network connected to 7 active power distribution networks. The results are compared to the ones obtained from a distributed method based on an Incident Command System (ICS) and a centralized control method. It turns out that the proposed method outperforms the other two competing methods.


Introduction


The ever-increasing advances in power electronics and utilization of electricity in industry necessitated alterations in power distribution systems in various countries, due to altering the operational strategies. Consequently, energy management is addressed at the highest levels of technology and engineering. It is studied as a major economic investment and commodity. Thus, one of the main objectives for power systems operators is the economic and safe operation. To this end, optimal scheduling and performance of the power systems are required. They have recently attracted the attention of many researchers. One of the tools to achieve the optimal performance and utilization of power systems is the Optimal Reactive Power Dispatch (ORPD). The problem of ORPD is a part of power system optimization problems in which, based on a series of constraints and control variables, specific objective functions are optimized. A review of the literature reveals three objective functions, as follows [1–4]: (i) improving the voltage profile; (ii) increasing the voltage stability margin; (iii) reducing active power losses. Thus, it is evident that reactive power significantly affects the major parameters of power systems operation. There are different methods to solve ORPD optimization problems. Typical classification of these methods is as follows: graphical, analytic or classic, specific, numerical, dynamic planning, heuristic methods. The details for each one of these subdivisions are presented in [5,6]. Heuristic methods are suitable to solve such kind of problems. Some examples for these methods include Genetic Algorithm (GA) [7,8], Tabu Search (TS) [9], Particle Swarm Optimization (PSO) [10,11], Gravitational Search Algorithm (GSA) [12], Artificial Bee Colony (ABC) aided by Differential Evolution (DE) [13], and Seeker Optimization Algorithm (SOA) [14].

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