# Controlled Fault Current Interruption Scheme for Improved Fault Prediction Accuracy
###### Abstract  
To enhance the accuracy and efficiency of controlled fault current interruption (CFI) in short-circuit current processing within power systems, a half-cycle elimination prediction algorithm and a double-sampling CFI sequence method are proposed in this study. By analyzing the non-periodic and periodic components of short-circuit currents, the half-cycle elimination method and fast Fourier transform are utilized to compute these two components, respectively. The double-sampling CFI sequence approach is designed to fully utilize the response and waiting times of relay protection. Following the first sampling to estimate the target zero-crossing point, the remaining response and waiting times are allocated for a second sampling and recalculation to enhance the precision of zero-crossing prediction. MATLAB R2023a is employed to conduct multi-scenario simulations, and the algorithm's performance is evaluated using actual recorded waveform data. The results demonstrate that the proposed algorithm accurately predicts the target zero-crossing point after a short circuit, with a computational error of less than 0.2 ms. Furthermore, the double-sampling sequence method is shown to improve the accuracy of open-circuit zero-crossing point calculations by an order of magnitude. This work provides a novel technical approach for the fast and precise handling of short-circuit faults in power systems. short-circuit current prediction; phase-controlled switching technique; half-period elimination method; recursive least squares method; double-sampling time +
Footnote †: journal: Applied sciences  
0000-0001-5000-0000-0000-0000-0000-0000-0000-0000-00000-0000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-000000-00000-000000-000000-000000-0000  
practice, short-circuit faults are mainly predicted by transient characteristics, and some restrictive measures are taken to reduce the hazards of short-circuit faults [6; 7]. When a short circuit occurs in a power system, the current will rise to ten or even hundreds of times its original value in a few microseconds, jeopardizing the safe operation of the power system [8; 9]. Therefore, it is of great significance to quickly and accurately calculate the short-circuit current parameters and predict the short-circuit current over the zero point to complete the CFI (controlled fault current interruption) to ensure the safe and stable operation of the power system, as well as to reduce the damage caused by short-circuit faults [10; 11]. In addition, there are interference problems, such as noise and harmonics in the actual power system, that also test the anti-harmonic and anti-noise abilities of the prediction algorithm [12]. Research on predicting short-circuit current crossing points by identifying short-circuit current waveform parameters is scarce, and most studies have limitations. The authors of [13; 14] used the least squares method to identify the short-circuit current parameters, but the algorithm ignores the effect of harmonic factors and has a large error. The study in [15] used an improved half-wave Fourier algorithm to process the current signal, but the algorithm does not take into account the effect of noise in testing and does not have a practical application value. The study in [16] proposes a safe point algorithm, which reacts quickly but does not take into account the attenuation of the DC component of the fault current. The current model is too simple and the over-zero point prediction accuracy is low. The authors of [17] propose an LSTM-based short-circuit current over-zero prediction method, but the calculation time of this method is too long and cannot meet the requirements of response speed in the field. In this paper, we propose an innovative half-cycle elimination method combining RLS (recursive least squares) and FFT (fast Fourier transform) to meet the stringent requirements of CFI for computational accuracy and speed.