Abstract
In this paper, a series arc was simulated under resistive load and motor load, which are mainly used in small ships, and the arc signal was analyzed using discrete wavelet transform. After calculating the correlation coefficient between the single arc pulse and the wavelet, Biorthogonal (bior) 3.1 was selected as the optimal mother wavelet, and the signal was analyzed using multiresolution analysis. From the results, arc signals were distributed in the detail components D2, D3, D4 and D5, corresponding to a frequency range of 19.5–312.5 kHz, with the optimal arc signal extracted based on these values. In addition, in order to distinguish between arc and normal conditions, signal energy was analyzed. By applying the magnitude and signal energy analysis method, the DC series arc generated in the power distribution system of a shipboard was identified.
Highlights
As reported by the Korea National Fire Data System, more than 500 cases of ship fires have occurred over the past five years
An experiment setup was configured to simulate a DC series arc on shipboards, which mainly consisted of an arc generator, resistive load and motor load
The arc current signal was detected by a HFCT and was analyzed by discrete wavelet transform (DWT)
Summary
As reported by the Korea National Fire Data System, more than 500 cases of ship fires have occurred over the past five years. The intrinsic behavior of the arc signal was analyzed by current entropy to distinguish between an arc fault and a normal condition [16] This method has a lower sampling frequency and a short time to detect arc faults, but it is easy to influence an arc signal by a noise signal depending on the surrounding environment [17]. This method identifies arc faults but it may be unreliable depending on the load types because the specific frequency range and spectrum value may depend on the loads For this reason, various methods for detection of DC series arcs are being studied to distinguish arc faults from normal conditions, and most of them involve complex and expensive devices [21,22]. The most important feature for AC arc detection is a
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
