Power systems are becoming more interconnected and complex. The distributed generation expands and spreads across the grids, reducing the distance between the load and the generation. In addition, several substations are aging after decades of operation and their equipment struggle to sustain the ever increasing fault levels. In this context, the fault current limiter (FCL) arrived as a solution to mitigate this problem. Considering the FCL devices, the resistive superconducting FCL (R-SFCL) is the most mature technology with potential to be produced in mass scale, due to its ability to quickly change its impedance during a fault current and its high current density capacity. In this paper, a novel R-SFCL topology is presented, which has an unique design that allows a compact size and the possibility of modulated assembly. These characteristics enable setups for various voltage and current levels. One advantage of this topology is the compromise between volume and high heat exchange that can reduce the recovery time under load. One bench prototype was modeled using the thermal–electrical analogy implemented in ATPDraw, tested in two different labs. Tests were performed at faults levels of 12 kA, 5 kA and 2.7 kA for 137 V. Measured and simulated results were compared, resulting in a relative error of less than 12%. Two contributions can be highlighted: the new design of the R-SFCL and the inclusion in the convection heat exchange model curves for the heating (during the quench) and cooling (after fault), which allows to predict the recovery under load.

电力系统变得越来越互连和复杂。分布式发电在网格上扩展和分布，从而减少了负载与发电之间的距离。此外，数个变电站经过数十年的运行正在老化，其设备仍在努力维持不断增长的故障水平。在这种情况下，故障电流限制器（FCL）作为缓解此问题的解决方案而来。考虑到FCL器件，电阻超导FCL（R-SFCL）是最成熟的技术，有潜力大规模生产，这是因为它具有在故障电流期间快速改变其阻抗的能力以及高电流密度的能力。在本文中，提出了一种新颖的R-SFCL拓扑，该拓扑具有独特的设计，可以实现紧凑的尺寸并可以进行模块化组装。这些特性使得可以进行各种电压和电流水平的设置。这种拓扑的一个优势是体积与高热交换之间的折衷，可以减少负载下的恢复时间。使用在两个不同实验室中测试过的ATPDraw中实现的热电模拟对一个工作台原型进行建模。在故障水平为12 kA的条件下进行测试，在137 V下为5 kA和2.7 kA 。比较了测量结果和模拟结果，相对误差小于12％。可以强调两个方面：R-SFCL的新设计以及对流换热模型曲线中加热（淬火期间）和冷却（故障之后）的对流，这可以预测负载下的恢复情况。