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The increase of current uniformity along of a resistive type superconductor fault current limiter (R-SFCL) in the design of this type of limiters is well perceived as an important issue. The non-uniform distribution of current in R-SFCL only increases the current in some superconducting regions, as a result, in the fault conditions, only certain parts of the superconductor undergo a phase change that increases the heat pressure in those areas and causes the breakdown and destruction of the device. In this paper, the current density distributions in common patterns used in R-SFCs constructions have been simulated and investigated. To this end, an effective model is proposed for R-SFCL to achieve the highest uniformity of current and harmonic phase change over superconductors compared to other patterns. The simulation results in the Ansys Maxwell Software advocate the appropriate and satisfying performance of the proposed model.

The neural network (NN)-based direct uncertainty quantification (UQ) methods have achieved the state of the art performance since the first inauguration, known as the lower-upper-bound estimation (LUBE) method. However, currently-available cost functions for uncertainty guided NN training are not always converging and all converged NNs are not generating optimized prediction intervals (PIs). Moreover, several groups have proposed different quality criteria for PIs. These raise a question about their relative effectiveness. Most of the existing cost functions of uncertainty guided NN training are not customizable and the convergence of training is uncertain. Therefore, in this paper, we propose a highly customizable smooth cost function for developing NNs to construct optimal PIs. The optimized average width of PIs, PI-failure distances and the PI coverage probability (PICP) are computed for the test dataset. The performance of the proposed method is examined for the wind power generation and the electricity demand data. Results show that the proposed method reduces variation in the quality of PIs, accelerates the training, and improves convergence probability from 99.2% to 99.8%.

Superconducting fault current limiters (SFCLs) are new high-precision and fast-response devices which help to reduce the fault current within the breaking capacity of the protective relays. Nevertheless, the reconfigurable structure of the distribution network can affect their performance negatively by changing the supplying path of the electrical loads and thus keeping SFCL in a useless point which cannot limit the high fault currents. This paper proposes an aggregated approach to solve the optimal placement of SFCLs considering the reconfiguration of feeders through the pre-located tie and sectionalizing switches. While SFCL placement problem aims to minimize the number of SFCLs and limit the high short circuit currents in the first seconds of the fault, the reconfiguration strategy is used to minimize the total grid costs incorporating the cost of power losses and customer interruptions. According to the high non-linearity and complexity of the proposed problem, social spider algorithm (SSA) with a two-phase modification method is developed to solve the proposed problem. The feasibility and performance of the proposed method are examined on an IEEE test system.