دانلود مقاله ISI انگلیسی شماره 47049
ترجمه فارسی عنوان مقاله

مدیریت زمان واقعی و طرح بهینه سازی تکاملی برای یک شبکه هوشمند، امن و انعطاف پذیر نسبت به انرژی پایدار

عنوان انگلیسی
A real-time management and evolutionary optimization scheme for a secure and flexible smart grid towards sustainable energy
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
47049 2014 9 صفحه PDF
منبع

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

Journal : International Journal of Electrical Power & Energy Systems, Volume 62, November 2014, Pages 540–548

ترجمه کلمات کلیدی
زمان بحرانی - مرکز کنترل - برآوردگر حالت توزیع شده - فاصله از گسل - الگوریتم ژنتیک - قدرت واکنش پذیری
کلمات کلیدی انگلیسی
Critical time; Control center; Distributed state estimator (DSE); Fault distance; Genetic Algorithm (GA); Reactive power
پیش نمایش مقاله
پیش نمایش مقاله  مدیریت زمان واقعی و طرح بهینه سازی تکاملی برای یک شبکه هوشمند، امن و انعطاف پذیر نسبت به انرژی پایدار

چکیده انگلیسی

Sustainable energy is the energy production without compromising the energy production for the future generations. The existing power grid model does not provide real-time information of transmission devices, security during emergency events, and frequency and voltage control. The proposed scheme consists of location-centric hybrid system architecture for the coordinated processing among proximate devices. The neighboring devices follow a collaboration algorithm to handle faulty and incomplete information. The proposed scheme also consists of distributed algorithm for the maintenance of the local state of the smart grid and a real-time accessibility control of transmission devices. The security of the system can be guaranteed by reconfiguration through power-electronics and switches. An embedded intelligence is inserted into the power-electronics to facilitate the reconfiguration of the system, and thereby ensuring security. A generalized optimization formulation determines the optimum location of the transmission devices. In this paper, Genetic Algorithm (GA) is used to handle the reactive power management. The use of GA decreases execution time of resource scheduling. This method performs better than the existing power grid models in terms of fault detection, degree of power saving due to power optimization, memory usage, consumer-GW (gateway) communication overhead, consumer computational overhead, and critical time.