Empirical Modeling of Superconducting Fault Current Limiter Using Support Vector Regression

The superconductor-triggered type fault current limiter (STFCL), which was developed by Korea Electric Power Corporation (KEPCO) and LS Industrial Systems (LSIS), is under operation for the verification test at KEPCO´s power testing center. The STFCL is composed of superconductor, fast switch and current limiting resistor. The fault current is suppressed after a half cycle by the method of a line commutation. In this paper, we investigated the empirical modeling of STFCL using principal components and auto-associative support vector regression (PCSVR) for the prediction and fault detection of the STFCL. Signals for the model are currents and voltages acquired from high-temperature superconductor (HTS), driving coil (DC) and current limiting resistor (CLR). After developing the empirical model we analyse the accuracy of the model. The results were compared with that of auto-associative neural networks (AANN). PCSVR showed much better performance in accuracy aspect. Moreover, this model can be used for the prognosis of STFCL system.