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Wide area monitoring, protection, and control application in islanding detection for grid integrated distributed generation: A review
Measurement and Control ( IF 2 ) Pub Date : 2021-04-15 , DOI: 10.1177/0020294021989768
Onkemetse Tshenyego 1 , Ravi Samikannu 1 , Bokani Mtengi 1
Affiliation  

The assimilation of Distributed Generation (DG) into the electric power system (EPS) has become more attractive as the world is following a trend to reduce greenhouse gas emissions by introducing more renewable energy forms resulting in high penetration scenarios. This high penetration of DGs brings several challenges to the protection philosophy of the EPS which compromises its reliability, availability, and efficiency. Under high DG penetration scenarios, conventional islanding detection methods (Idms) fail to detect an island as the grid loses its inertia to leverage a significant frequency and voltage mismatch necessary for Idms to effectively detect an islanding event. This has given rise to the birth of Artificial Intelligent (AI) methods that are found to perform better in islanding detection. AI Idms are computationally intensive and require a lot of data to operate accurately. Because the computational burden of these methods requires fast computing hardware, the current trend of AI Idms are integrated with Wide Area Monitoring, Protection, and Control (WAMPAC) system. This paper aims at reviewing all these Idms and the WAMPAC’s system latency when hosting AI Idms which are currently the best in islanding detection. This is done to determine if the WAMPAC system latency plus Idms computational time meet the islanding detection time specified by the IEEE Standard 1547 framework.



中文翻译:

广域监视,保护和控制在电网集成分布式发电孤岛检测中的应用:综述

分布式发电(DG)与电力系统(EPS)的同化已变得更具吸引力,因为世界正遵循一种趋势,即通过引入更多可再生能源形式来实现高渗透率情景,从而减少温室气体排放。DG的这种高渗透率给EPS的保护理念带来了几项挑战,从而损害了EPS的可靠性,可用性和效率。在高DG渗透情况下,传统的孤岛检测方法(Idms)无法检测到孤岛,因为电网失去了惯性,无法充分利用Idms有效检测孤岛事件所需的频率和电压失配。这催生了人工智能(AI)方法,这些方法在孤岛检测中表现更好。AI Idms占用大量计算资源,需要大量数据才能准确运行。由于这些方法的计算负担需要快速的计算硬件,因此AI Idms的当前趋势已与广域监视,保护和控制(WAMPAC)系统集成在一起。本文旨在回顾所有这些Idms和WAMPAC在托管AI Idms时的系统延迟,这些AI Idms目前是孤岛检测中最好的。这样做是为了确定WAMPAC系统延迟加Idms计算时间是否满足IEEE标准1547框架指定的孤岛检测时间。本文旨在回顾所有这些Idms和WAMPAC在托管AI Idms时的系统延迟,这些AI Idms目前是孤岛检测中最好的。这样做是为了确定WAMPAC系统延迟加Idms计算时间是否满足IEEE标准1547框架指定的孤岛检测时间。本文旨在回顾所有这些Idms和WAMPAC在托管AI Idms时的系统延迟,这些AI Idms目前是孤岛检测中最好的。这样做是为了确定WAMPAC系统延迟加Idms计算时间是否满足IEEE标准1547框架指定的孤岛检测时间。

更新日期:2021-04-15
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