Abstract The capability of network configuration change and high penetration of distributed generations (DGs) are two important characteristics of modern distribution networks. The configuration change and connection/disconnection of DGs result in the variation of fault currents’ magnitude and direction besides changing the role of protective devices as primary or back up protection. This fact can make the protection coordination complicated or even impossible in some cases. One of the solutions to this problem is to use a setting group (SG)-based adaptive protection schemes. SG-based protective schemes protect various network states (different configurations and connection/disconnection of DGs) using a limited number of SGs. Therefore, it is needed to propose an algorithm to specify the settings of each SG and the determination of SG selection in different network states (NSTs). In this paper, previous attempts in the utilization of SG capability of Overcurrent relays (OCRs) are examined and a novel approach is proposed which is capable of protecting the same network with lower delay in relays' operation times although it requires a fewer number of SGs. In this paper, a new method based on integer linear programming (ILP) and particle swarm optimization (PSO) is utilized to specify the proper SG to activate for each relay in each NST, in order to minimize the total operating time of OCRs, considering coordination constraints. The efficiency of the proposed method in comparison to the previous methods is demonstrated by applying on a sample distribution network.

中文翻译：

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基于继电器整定组独立变化的有源配电网过流继电器自适应协调

摘要 网络配置变化能力和分布式电源（DG）的高渗透率是现代配电网络的两个重要特征。DG 的配置变化和连接/断开除了改变保护装置作为初级或后备保护的作用外，还会导致故障电流大小和方向的变化。在某些情况下，这一事实会使保护协调变得复杂甚至不可能。该问题的解决方案之一是使用基于设置组 (SG) 的自适应保护方案。基于 SG 的保护方案使用有限数量的 SG 保护各种网络状态（DG 的不同配置和连接/断开连接）。所以，需要提出一种算法来指定每个SG的设置以及不同网络状态（NST）下SG选择的确定。在本文中，研究了先前在利用过流继电器 (OCR) 的 SG 能力方面的尝试，并提出了一种新方法，该方法能够以较低的继电器操作时间延迟保护同一网络，尽管它需要较少数量的 SG . 在本文中，一种基于整数线性规划 (ILP) 和粒子群优化 (PSO) 的新方法用于为每个 NST 中的每个继电器指定适当的 SG 激活，以最小化 OCR 的总操作时间，考虑协调约束。与以前的方法相比，所提出的方法的效率通过应用于样本分配网络来证明。