Journal
IEEE ACCESS
Volume 9, Issue -, Pages 166249-166261Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3135526
Keywords
Feature extraction; Circuit faults; Fault diagnosis; Neurons; Inverters; Frequency-domain analysis; Switches; Fault diagnosis; DC~series arc; machine learning; different domains
Categories
Funding
- National Research Foundation of Korea (NRF) - Korea government (MSIT) [2020R1A2C1013413]
- Technology Development Program to Solve Climate Changes through the National Research Foundation of Korea (NRF) - Ministry of Science, ICT [2021M1A2A2060313]
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This paper presents an analysis and comparison of DC series arc fault detection methods using various artificial intelligence algorithms, recommends combinations of feature parameters and AI techniques for effective detection, and summarizes the performance and effectiveness of different combinations.
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.
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