DC Series Arc Fault Diagnosis Scheme Based on Hybrid Time and Frequency Features Using Artificial Learning Models

Abstract

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.

Advanced Learning Technique Based on Feature Differences of Moving Intervals for Detecting DC Series Arc Failures

“…For uncompromising data acquisition, authors employed an oscillosc stunning 250 kHz sampling frequency, capturing even the most fleeting o ances. The sampling rate of 250 kHz was selected based on findings from gations into arc faults in DC networks [27][28][29][30]. While a higher sampling ra more data points per unit time, it could also increase processing time and burden.…”

“…These inverters, components, played a crucial role in transforming DC signals into their AC To ensure precise control, we harnessed the power of space vector modula For uncompromising data acquisition, authors employed an oscilloscope boasting a stunning 250 kHz sampling frequency, capturing even the most fleeting of electrical nuances. The sampling rate of 250 kHz was selected based on findings from recent investigations into arc faults in DC networks [27][28][29][30]. While a higher sampling rate would yield more data points per unit time, it could also increase processing time and computational burden.…”

Various Feature-Based Series Direct Current Arc Fault Detection Methods Using Intelligence Learning Models and Diverse Domain Exclusion Techniques

“…Machine learning algorithms have shown promise in DC arc fault detection. Nevertheless, current methodologies frequently concentrate exclusively on time or frequency domain currents, overlooking the necessity for inclusive preprocessing of signals [18][19][20][21][22][23][24][25][26][27][28], although an approach with simple indexes has been tried for DC arc detection [19]. This research presents a novel methodology to detect arc fault recognition by extracting and utilizing various key features for DC arc detection.…”