Short-Circuit Fault Current Parameter Prediction Method Based on Ultra-Short-Time Data Window

Abstract

The prediction of short-circuit current parameters is essential for the adoption of short-circuit fault limiting techniques and the reliable cut-off of circuit breakers. In order to quickly and accurately predict the short-circuit current waveform parameters, a short-circuit fault current prediction method based on ultra-short-time data windows (UDWs) is proposed. First, a mathematical model for describing short-circuit faults is constructed and the characteristics of short-circuit currents are analyzed. Then, the principle of the UDW method for predicting short-circuit current waveform parameters is derived, the correctness of the principle is verified by setting-up an ideal signal through simulation, and the exponential and linear expressions fitted to the curve are analyzed and compared with the improved half-wave Fourier method for predicting current parameters. Finally, trend filtering technology is proposed to eliminate high-frequency interference and white noise interference. The results show that the ultra-short-time data window method can quickly and accurately predict the short-circuit current waveform parameters, where the exponential expression is a better fit to the waveform, and the trend filtering technique enables the elimination of high-frequency and white noise interference in the initial stages of prediction.