Optimal Design of a Band Pass Filter and an Algorithm for Series Arc Detection

Detection and analysis of series arcs is significantly meaningful for preventing arc-caused electrical fires in advance. However, the improvement of arc detection sensitivity and the discrimination of arc conditions are still challenges when developing an arc fault detector. In this paper, arc signals in various loads with three major incomplete connection states were detected and further analyzed using the discrete wavelet transform. It was verified that the db13 was the optimal mother wavelet to analyze the arc pulses and the decomposed signals in the detail components of D5, D6, D7, and D8 were related with arc phenomena. Therefore, a band pass filter with a frequency from 2.4 to 39 kHz was designed, which can extract arc signals while eliminating the AC mains current and noise generated in loads. By investigating the arc signal energy as well as the arc pulse counts that were important parameters of arc occurrence, an arc diagnosis algorithm was developed based on LabVIEW program for electrical fire prevention.

[1]  Chi-Jui Wu,et al.  Smart detection technology of serial arc fault on low-voltage indoor power lines , 2015 .

[2]  M. Teguar,et al.  Characterization of discharges on non-uniformly polluted glass surfaces using a wavelet transform approach , 2013, IEEE Transactions on Dielectrics and Electrical Insulation.

[3]  Hsueh-Hsien Chang,et al.  Statistical Feature Extraction for Fault Locations in Nonintrusive Fault Detection of Low Voltage Distribution Systems , 2017 .

[4]  Jose A. Antonino-Daviu,et al.  Comparison of different wavelet families for broken bar detection in induction motors , 2015, 2015 IEEE International Conference on Industrial Technology (ICIT).

[5]  Kon B. Wong,et al.  More about arc-fault circuit interrupters , 2003 .

[6]  Luca Zarri,et al.  Advanced Diagnosis of Outer Cage Damage in Double-Squirrel-Cage Induction Motors Under Time-Varying Conditions Based on Wavelet Analysis , 2014, IEEE Transactions on Industry Applications.

[7]  Jianhong Yang,et al.  A Novel Arc Fault Detector for Early Detection of Electrical Fires , 2016, Sensors.

[8]  Ferhat Ucar,et al.  Power Quality Event Detection Using a Fast Extreme Learning Machine , 2018 .

[9]  Tang Ju,et al.  Application of Complex Wavelet Transform to Suppress White Noise in GIS UHF PD Signals , 2007, IEEE Transactions on Power Delivery.

[10]  Guoming Wang,et al.  Disturbance Elimination for Partial Discharge Detection in the Spacer of Gas-Insulated Switchgears , 2017 .

[11]  T.J. Schoepf,et al.  Arc fault detection scheme for 42-V automotive DC networks using current shunt , 2006, IEEE Transactions on Power Electronics.

[12]  Won-Sik Moon,et al.  Ignition characteristics of residential series arc fault in 220V HIV wire , 2014, 2014 IEEE Industry Application Society Annual Meeting.

[13]  J.J. Shea.  Conditions for Series Arcing Phenomena in PVC Wiring , 2007, IEEE Transactions on Components and Packaging Technologies.

[14]  Sung-Wook Kim,et al.  Optimization of wavelet and thresholding for partial discharge detection under HVDC , 2017, IEEE Transactions on Dielectrics and Electrical Insulation.

[15]  Ying-Hong Lin,et al.  A new PMU-based fault detection/location technique for transmission lines with consideration of arcing fault discrimination-part I: theory and algorithms , 2004 .

[16]  J. Antonino-Daviu,et al.  Application and Optimization of the Discrete Wavelet Transform for the Detection of Broken Rotor Bars in Induction Machines , 2006 .

[17]  Yskandar Hamam,et al.  Diagnosis of stator shorted-turn faults in induction machines using discrete wavelet transform , 2017 .