系列(地层学)
方案(数学)
断层(地质)
计算机科学
弧(几何)
人工智能
时间序列
模式识别(心理学)
机器学习
工程类
数学
地质学
地震学
机械工程
数学分析
古生物学
作者
Hoang-Long Dang,Sangshin Kwak,Seungdeog Choi
出处
期刊:Machines
[MDPI AG]
日期:2024-02-01
卷期号:12 (2): 102-102
被引量:2
标识
DOI:10.3390/machines12020102
摘要
DC series arc faults pose a significant threat to the reliability of DC systems, particularly in DC generation units where aging components and high voltage levels contribute to their occurrence. Recognizing the severity of this issue, this study aimed to enhance DC arc fault detection by proposing an advanced recognition procedure. The methodology involves a sophisticated combination of current filtering using the Three-Sigma Rule in the time domain and the removal of switching noise in the frequency domain. To further enhance the diagnostic capabilities, the proposed method utilizes time and frequency signals generated from power supply-side signals as a reference input. The time–frequency features extracted from the filtered signals are then combined with artificial learning models. This fusion of advanced signal processing and machine learning techniques aims to capitalize on the strengths of both domains, providing a more comprehensive and effective means of detecting arc faults. The results of this detection process validate the effectiveness and consistency of the proposed DC arc failure identification schematic. This research contributes to the advancement of fault detection methodologies in DC systems, particularly by addressing the challenges associated with distinguishing arc-related distortions, ultimately enhancing the safety and dependability of DC electrical systems.
科研通智能强力驱动
Strongly Powered by AbleSci AI