Abstract
The unwanted electric discharge usually relates to arc phenomena between two connectors. The energy from an arc might fuse the electric wiring and be responsible for a fire. Various researches have been investigated for safety operations to improve detected techniques for arc diagnosis. There are two types of arc faults: parallel and series arcs. A parallel arc happens among two electrical lines, or line and ground, due to degrading insulation or contamination. On the other hand, a series arc might result from releasing connections in the wiring. The system’s current can be significantly increased by parallel arc fault compared with the series arc. In this work, the electrical behavior of the system is investigated during parallel arc faults to understand the arcing characteristics from different cases, identify electrical characteristics that are useful and reliable for the diagnosis process, and determine the location of the fault based on current or voltage of the faulted system. Eight learning techniques are adopted to detect arc fault in this study. Parallel arc signals were analyzed in the time and frequency domains, and unique characteristics of the current are extracted using Fourier analysis as an indicator for diagnosing an arc fault. This research can be used to improve arc-fault detector reliability and robustness.
Highlights
DC networks are widely used in aerospace, photovoltaic systems, data storage, electric transportation, and various areas
If the arc current is in the safety device rated current range, the failure might not be identified in time
The parallel arc fault can be more dangerous than the series arc because it can increase the current in the system
Summary
DC networks are widely used in aerospace, photovoltaic systems, data storage, electric transportation, and various areas. A short-observation-window singular value decomposition and reconstruction algorithm are proposed to identify AC series arc fault [20] This method guarantees a high diagnosis rate with different load types, the complexity and the need for additional hardware are the limitations of this proposed method. These studies focus only on series arc fault, whereas the application of AI for the parallel arc is not thoroughly investigated.
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