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

Mengjiao Wang (), Xinlao Wei and Zhihang Zhao
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Mengjiao Wang: Key Laboratory of Engineering Dielectrics and Its Application, Ministry of Education, School of Electrical and Electronics Engineering, Harbin University of Science and Technology, Harbin 150080, China
Xinlao Wei: Key Laboratory of Engineering Dielectrics and Its Application, Ministry of Education, School of Electrical and Electronics Engineering, Harbin University of Science and Technology, Harbin 150080, China
Zhihang Zhao: Key Laboratory of Engineering Dielectrics and Its Application, Ministry of Education, School of Electrical and Electronics Engineering, Harbin University of Science and Technology, Harbin 150080, China

Energies, 2022, vol. 15, issue 23, 1-15

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.

Keywords: ultra-short-time data windows; trend filtering technology; current amplitude; high-frequency interference; white noise interference (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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