Abstract
Series arc faults are becoming more dangerous in DC systems. Without detecting in time and separation correctly, these fault events can cause electrical fires or explosions, creating a massive threat to people’s safety and properties. This paper presents an analysis and comparison of DC series arc fault detection using various artificial intelligence (AI) algorithms in DC systems. The combinations of six feature parameters in both time and frequency domains with various AI techniques are recommended to detect DC series arc fault effectively. The performance and effectiveness of different combinations between feature parameters and learning techniques are summarized and discussed. Finally, practical challenges are identified, and suitable combinations of feature parameters and learning techniques are recommended for different operation conditions.
Highlights
With the rising of pollution concerns on the global environment, green energies have become a potential candidate to replace traditional energies that come from fossil fuels
deep learning (DL) techniques are more successful when frequency-domain input is applied than machine learning (ML) algorithms in all switching frequency ranges
ML techniques are more accurate than DL algorithms when time-domain input is employed
Summary
With the rising of pollution concerns on the global environment, green energies have become a potential candidate to replace traditional energies that come from fossil fuels. Positive results, such as the combination of the wavelet packet decomposition and support vector machine (SVM) algorithm for DC arc fault diagnosis [12], the hidden Markov model (HMM) was adopted for correctly detecting series arc faults using the maximum likelihood [13] Several characteristics, such as the variations of current and high-frequency components, are obtained and used for training models centered on weighted least squares SVM techniques to detect series arc [14].
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