مشخصات مقاله | |
ترجمه عنوان 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 |
انتشار | مقاله سال 2019 |
تعداد صفحات مقاله انگلیسی | 21 صفحه |
هزینه | دانلود مقاله انگلیسی رایگان میباشد. |
پایگاه داده | نشریه الزویر |
نوع نگارش مقاله |
مقاله پژوهشی (Research Article) |
مقاله بیس | این مقاله بیس نمیباشد |
نمایه (index) | Scopus – Master Journals List – DOAJ |
نوع مقاله | ISI |
فرمت مقاله انگلیسی | |
ایمپکت فاکتور(IF) |
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 |
مدل مفهومی | ندارد |
پرسشنامه | ندارد |
متغیر | دارد |
رفرنس | دارد |
رشته های مرتبط | برق |
گرایش های مرتبط | توزیع و انتقال، مهندسی کنترل، مهندسی الکترونیک، الکترونیک قدرت |
نوع ارائه مقاله |
ژورنال |
مجله | علوم مهندسی و فناوری، یک مجله بین المللی – Engineering Science And Technology, An International Journal |
دانشگاه | Shahrood University of Technology, Shahrood, Iran |
کلمات کلیدی | 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) |
شناسه دیجیتال – doi |
https://doi.org/10.1016/j.jestch.2019.03.003 |
کد محصول | E12951 |
وضعیت ترجمه مقاله | ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید. |
دانلود رایگان مقاله | دانلود رایگان مقاله انگلیسی |
سفارش ترجمه این مقاله | سفارش ترجمه این مقاله |
فهرست مطالب مقاله: |
Abstract
1- Introduction 2- Proposed structure 3- Simulation results 4- Conclusion References |
بخشی از متن 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]. |