Bershadsky I.А., Glаdkov A.J., Zgarbul A.V., Shlepnjov S.V., Myh A.D. Method of Series arc Fault Detection Using Discrete Wavelet Transform Algorithm

Abstract

Full Text

In order to eliminate false alarms of the arc fault detection device (AFDD), a research was conducted and combinations of existing methods of processing data entering the microcontroller were considered, which may differ significantly from the type of load connected to the network. As a result of the research, a new method for identifying sequential arc circuits in the electrical network of residential buildings based on discrete wavelet transform (DWT) has been developed. The method includes a discrete-time analysis of experimental current curves with arc breakdowns in the mathematical package Matlab, as well as the extraction of signs of arc breakdown using a signal processing method called wavelets of the Daubechiesdb4 family. Verification and comparative analysis of the sequential arc breakdown detection model is considered on the example of two typical household electrical network loads: a 2.2 kW heater and a vacuum cleaner with a triac power regulator. It is established that arc breakdowns have unique features in their current curves, namely: the level of detail coefficients normalized in relative units of CDDWT in the time domain at a sampling frequency of 10 kHz shows the values: 1 level is from 0.2 to 0.5 and higher, 2 level is from 0.05 to 0.2 and higher. It has been established that the level of detail coefficients normalized in relative units in the time domain at a sampling frequency of 10 kHz at normal operation at each scale relative to the scale is level 1 < 0.1 and level 2< 0.04. This will allow us to characterize the intensity of sparking by the SAF matrix, the rows of which correspond to the level of normalized cd1, and the columns correspond to the half–period number 1..10.

Keywords

arc fault, electrical network, electric power consumer, wavelet transform, transient, current strength, sampling rate, microcontroller

Ilya А. Bershadsky D.Sc. (Engineering), Associate Professor, Head of Industrial and Municipal Power Supply Department, Donetsk National Technical University, Donetsk, Russia, This email address is being protected from spambots. You need JavaScript enabled to view it., https://orcid.org/0000-0001-7383-3415

Aleksandr Yu. Glаdkov Ph.D. (Engineering), Laboratory Chief, Scientific Research Laboratory of Intrinsic Safety, Electrical Facilities Department, Makeevka Scientific and Research Institute for Mine Safety, Makeevka, Russia, This email address is being protected from spambots. You need JavaScript enabled to view it., https://orcid.org/0009-0000-3979-0626

Andrej V. Zgarbul Ph.D. (Engineering), Associate Professor, Industrial and Municipal Power Supply Department, Donetsk National Technical University, Donetsk, Russia, This email address is being protected from spambots. You need JavaScript enabled to view it., https://orcid.org/0009-0001-3049-2291

Sergej V. Shlepnjov Ph.D. (Engineering), Associate Professor, Dean of Intelligent Electrical Power Engineering and Robotic Engineering, Donetsk National Technical University, Donetsk, Russia, This email address is being protected from spambots. You need JavaScript enabled to view it.

Anatolij D. Mykh Postgraduate Student, Industrial and Municipal Power Supply Department, Donetsk National Technical University, Donetsk, Russia, This email address is being protected from spambots. You need JavaScript enabled to view it.

1. Gordienko D.M. Pozhary i pozharnaya bezopasnost v 2019 godu. Statisticheskiy sbornik. [Fires and fire safety in 2019. Statistical collection]. Moscow, VNIIPO Publ., 2020. 80 p. (In Russian)

2. Monakov V.K. Development of a protection device against arc faults. Pozharovzryvobezopasnost [Fire and Explosion Safety], 2014, vol. 23, no. 8, pp. 27-31. (In Russian)

3. Arc fault protection for home: AFDD or UZM 50/UZIS. Available at: https://electrikblog.ru/zashchita-ot-dugovogo-zamykaniya-dlya-doma/. (accessed 11 November 2023). (In Russian)

4. Dongwei Li, Zhengxiang Song, Jianhua Wang, Yingsan Geng, Huilin Chen, Li Yu, Bo Liu A. Method for Residential Series Arc Fault Detection and Identification, Electrical Contacts, Proceedings of the Annual Holm Conference on Electrical Contacts. IEEE, 2009, pp. 7-13. doi: 10.1109/HOLM.2009.5284428

5. Yang K., Zhang R., Chen S., Zhang F., Yang J., Zhang X. Series Arc Fault Detection Algorithm Based on Autoregressive Bispectrum Analysis. Algorithms. 2015, no. 8(4), pp. 929-950. doi: 10.3390/a8040929

6. Wang Sh.-Ch., Wu Ch.-J., Wang Y.-J. Detection of Arc Fault on Low Voltage Power Circuits in Time and Frequency Domain Approach. International Journal of Circuits, Systems and Signal Processing. 2012, no. 6(5), pp. 324-331.

7. M. Rashevskaya, A. Kulikov, M. Tibrayev, A. Gudoghnikov: Arc Faults in Low-Voltage Distribution Networks, Proceedings of the 17th Conference on Electrical Machines, Drives and Power Systems (ELMA). IEEE, 2021, pp. 1-5. doi: 10.1109/ELMA52514.2021.9503046

8. Dyakonov V.P. Veivlety. Ot teorii k praktike. [Wavelets. From theory to practice]. Moscow, SOLON-Press Publ., 2010. 400 p. (In Russian)

9. Qi P., Jovanovic S., Lezama J., Schweitzer P. Discrete Wavelet Transform optimal parameters estimation for arc fault detection in low-voltage residential power networks. Electric Power Systems Research. 2017, no. 143, pp. 130-139. doi: 10.1016/J.EPSR.2016.10.008

10. MathWorks Help Center. Wavedec. Available at: https://www.mathworks.com/help/wavelet/ref/wavedec.html. (accessed 11 November 2023)

 

Bershadsky I.А., Glаdkov A.Yu., Zgarbul A.V., Shlepnjov S.V., Myh A.D. Method of Series arc Fault Detection Using Discrete Wavelet Transform Algorithm. Elektrotekhnicheskie sistemy i kompleksy [Electrotechnical Systems and Complexes], 2023, no. 4(61), pp. 76-81. (In Russian). https://doi.org/10.18503/2311-8318-2023-4(61)-76-81

Details
Hits: 1914