Evaluation Of Digital Wavelet Filter On Low Voltage Arcing Detection Equipment

Anton Putra Widyatama, Dimas Anton Asfani


Arcing is one of the causes of fire disasters that occur quite a lot in the world. The danger of arcing causes enormous losses. The fault current caused by arcing usually occurs in a very short duration, so that safety equipment such as Miniature Circuit Breaker (MCB) and Fuse cannot detect the disturbance. If arcing takes place continuously, there will be heat that can damage the equipment and cause a fire. In this study, an evaluation of the use of mother wavelets in the Discrete Wavelet Transform (DWT) method on low voltage detection equipment will be carried out. DWT is a mathematical function that is the most successful in the field of signal processing. Signal processing using the DWT method will be analyzed and compared its performance with mother wavelets Daubechies-1, Daubechies-4, Coiflet-4, and Symlet-4. Then applied to low voltage arcing detection equipment. From the results of testing and analysis obtained the mother wavelet Daubechies-4 produces a high level of accuracy and sensitivity. Mother wavelet Coiflet-4 and Symlet-4 produce the lowest level of accuracy at 400W and 700W load. Meanwhile, on the mother wavelet Daubechies-1, the normal signal amplitude value is close to the arcing signal amplitude value, so it will be difficult to determine the threshold value. In this study, the results obtained are arcing detection equipment using the mother wavelet Daubechies-4 method can detect arcing disturbances very well and produces a high level of accuracy and sensitivity compared to the mother wavelet Daubechies-1, Coiflet-4 , and Symlet-4.

Full Text:



“Indonesian Electrification Ratio,” Ministry of Energy and Mineral Resources of the Republic of Indonesia, May 2020. Accessed on: May 5, 2020. Available at: https://geoportal.esdm.go.id/project-strategis/.

Jakarta Fire, “Fire Statistics Based on Causes,” Jakarta Fire, DKI Jakarta, Annual Report, 2019. Accessed on: April 28, 2020. Available at: https://www.jakartafire.ne /statistics.

K.Mishra, A.Routray, and A. K. Pradhan, “Detection of Arcing in Low Voltage Distribution Systems”, IEEE Region 10 Colloquium and the Third International Conference on Industrial and Information Systems 2018, pp. 1-3.

National Fire Protection Association, “NFPA 70E: Standard for Electrical Safety in the Workplace”, NFPA 2014.

G. Artale, A. Cataliotti, V. Cosentino, D. Di Cara, S. Nuccio and G. Tinè, "Arc Fault Detection Method Based on CZT Low-Frequency Harmonic Current Analysis," in IEEE Transactions on Instrumentation and Measurement, vol. 66, no. 5, pp. 888-896, May 2017.

Bang, Sun-Bae & Park, Chong-Yeun & Jang, Mog-Soon & Choi, Won-HO. (2015). Analysis of Series Arc-Fault Signals Using Wavelet Transform From Non-linear Loads. The Transactions of The Korean Institute of Electrical Engineers. 57.

Riza Fakhroun Nisa', Dimas Anton Asfani, and I Made Yulistya Negara, "Analysis of Electric Arc Characteristics at Low Voltage Due to the Effect of Line Impedance Using Haar Wavelet Transformation" Journal of Engineering Pomits Vol. 1, No. 1, (2015)

Tammy Gammon, John Matthews, “The Historical Evolution of Busur api-Fault Models for Low-Voltage Systems”, IEEE

Zhen,Cao,"Simple Analysis of the Measurement Methods of Arc Fault". International Conference on Intelligent Systems Design and Engineering Applications.2016. page 914-917

Wu,Yuan, "A Method for Arc Fault Detection Based on the Analysis of Signal's Characteristic Frequency Band with Wavelet Transform". International Conference on Electric Power Equipment, Japan.2015. Hal 1-4

Wang,Zhang., “Arc Fault and Flash Signal Analysis in DC Distribution Systems Using Wavelet Transformation”. IEEE Transactions on Smart Grid.2015. Hal 1-9.

D.A Asfani, Syafaruddin, M.H Purnomo, T. Hiyama,"Temporary Short Circuit Detection in Induction Motor Winding Using Second Level Haar-Wavelet Transform", IEEJ Transactions on Industry Applications, Volume 131, Issue 9, pp. 1093-1102, 2014.

C. E. Bire And B. Cahyono, “Denoising in Image Using Wavelet Transformation,” Semin. Nas. technol. inf. and Commune. Terap., Vol. 2012, No. Semantics, Pp. 487–493, 2015

Zhang, Bai-Ling, Haihong Z., and Shuzi S.G., 2014, “Face Recognition by Apllying Wavelet Subband Representation and Kernel Associtive Memory,” IEEE Transactions of Neural Network, Vol. 15. No.1.

Uyulan, Çağlar & Erguzel, Turker. (2016). Comparison of Wavelet Families for Mental Task Classification.3. 1-6. 10.5455/JNBS.1454666348.

DOI: https://doi.org/10.12962/jaree.v7i2.311


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.