دانلود مقاله ISI انگلیسی شماره 48040
ترجمه فارسی عنوان مقاله

بهینه سازی مبتنی بر مدل آنفولانزا خوکی مقابله ای تحت شار با قرنطینه برای تخصیص بهینه DG در شبکه توزیع شعاعی

عنوان انگلیسی
Quasi-Oppositional Swine Influenza Model Based Optimization with Quarantine for optimal allocation of DG in radial distribution network
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
48040 2016 26 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : International Journal of Electrical Power & Energy Systems, Volume 74, January 2016, Pages 348–373

ترجمه کلمات کلیدی
تولید پراکنده؛ تلفات توان؛ بهینه سازی با قرنطینه مدل آنفولانزا خوکی مقابله ای تحت شار(QOSMIO-Q)؛ مشخصات ولتاژ؛ پایداری ولتاژ
کلمات کلیدی انگلیسی
Distributed generations; Power losses; Quasi-Oppositional Swine Influenza Model Based Optimization with Quarantine (QOSIMBO-Q); Voltage profile; Voltage stability
پیش نمایش مقاله
پیش نمایش مقاله  بهینه سازی مبتنی بر مدل آنفولانزا خوکی  مقابله ای تحت شار با قرنطینه برای تخصیص بهینه DG در شبکه توزیع شعاعی

چکیده انگلیسی

Optimal allocation of Distributed Generations (DGs) is one of the major problems of distribution utilities. Optimum locations and sizes of DG sources have profoundly created impact on system losses, voltage profile, and voltage stability of a distribution network. In this paper Quasi-Oppositional Swine Influenza Model Based Optimization with Quarantine (QOSIMBO-Q) has been applied to solve a multi-objective function for optimal allocation and sizing of DGs in distribution systems. The objective is to minimize network power losses, achieve better voltage regulation and improve the voltage stability within the frame-work of the system operation and security constraints in radial distribution systems. The limitation of SIMBO-Q algorithm is that it takes large number of iterations to obtain optimum solution in large scale real systems. To overcome this limitation and to improve computational efficiency, quasi-opposition based learning (QOBL) concept is introduced in basic SIMBO-Q algorithm. The proposed QOSIMBO-Q algorithm has been applied to 33-bus and 69-bus radial distribution systems and results are compared with other evolutionary techniques like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), combined GA/PSO, Teaching Learning Based Optimization (TLBO) and Quasi-Oppositional Teaching Learning Based Optimization (QOTLBO). Numerical studies represent the effectiveness and out-performance of the proposed QOSIMBO-Q algorithm.