Intelligent surrogate model of a high-temperature superconducting cable for reliable energy delivery: short-circuit fault performance

Abstract

High-temperature superconducting (HTS) cables are promising solutions for electric power transmission of renewable energy resources, where their fault performance study is vital to avoid power interruptions in the grid. In this study, a fast intelligent surrogate model was presented to estimate the fault performance of a 22.9 kV/50 MW HTS cable to make fast fault performance analysis of the HTS cables possible during the design stage. Different fault scenarios were considered under different fault durations, fault resistances, and types of faults. Then, the fault energy, fault current, fault type, fault duration, and fault resistance were fed into the surrogate model as inputs. The outputs were the temperature of the rare-earth barium copper oxide (ReBCO) tapes, the former temperature, the ReBCO layer current, and the total resistance of each phase. For surrogate modelling, cascade forward neural networks (CFNNs) were used. The results show that the CFNN-based model estimated the fault performance of the cable with an average accuracy of 99.1%. Finally, the impact of considering fault energy, fault current, and both, as the inputs of the models, on the final accuracy were explored. The results show that by considering the fault energy, the accuracy of the surrogate model can be increased.