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An Intelligent Adaptive Overcurrent Protection System for an Automated Microgrid in Islanded and Grid-Connected Operation Modes | ||
International Journal of Nonlinear Analysis and Applications | ||
مقاله 29، دوره 11، شماره 1، تیر 2020، صفحه 381-394 اصل مقاله (7.9 M) | ||
شناسه دیجیتال (DOI): 10.22075/ijnaa.2020.4320 | ||
نویسندگان | ||
Mahmood Sadoughi؛ Mehrdad Hojjat* ؛ Mohamad Hosseini Abardeh | ||
Department of Electrical and Electronic Engineering, Shahrood Branch, Islamic Azad University, Shahrood, Iran | ||
تاریخ دریافت: 19 آذر 1398، تاریخ بازنگری: 14 بهمن 1398، تاریخ پذیرش: 22 بهمن 1398 | ||
چکیده | ||
As the penetration of distributed generation sources increases at the distribution level, the necessity of designing microgrids is highlighted. Microgrids can operate in either islanded (separated from the utility grid) or grid-connected mode. Apart from the technical and economic benefits of microgrids, they may cause some protection issues in distribution systems. The most important of these issues is the difference between the short circuit levels in islanded and grid-connected operation modes. In this paper, an intelligent overcurrent relay that can adapt to the microgrid operation mode is proposed; that is, the relay detects the microgrid operation mode by means of an adaptive algorithm. The proposed adaptive method optimizes the accuracy and speed of the algorithm for detecting the microgrid operation mode. Then, the optimal settings that have been already stored in the relay are activated according to the microgrid operation mode. All internal processes of the relay use the voltage and current sampled at the relay location. As a result, the relay can be used in all networks without communication infrastructures. Finally, the proposed method is implemented in a sample microgrid. The superiority of the intelligent relay performance over conventional overcurrent relays is shown by numerical results. | ||
کلیدواژهها | ||
Overcurrent Relay؛ Microgrid؛ Islanded operation؛ Adaptive Algorithm؛ Coordination؛ Convolutional Neural Network | ||
آمار تعداد مشاهده مقاله: 17,140 تعداد دریافت فایل اصل مقاله: 2,023 |