Classification of diagnostic features of transient signals in the electric power industry

Rustem Khuziashev; Igor Kuzmin; Iluza Irkagalieva

doi:10.1051/e3sconf/202128801036

All issues

Volume 288 (2021)

E3S Web Conf., 288 (2021) 01036

References

Open Access

Issue		E3S Web Conf. Volume 288, 2021 International Symposium “Sustainable Energy and Power Engineering 2021” (SUSE-2021)


Article Number		01036
Number of page(s)		8
DOI		https://doi.org/10.1051/e3sconf/202128801036
Published online		14 July 2021

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[1] L. Hongyu, U. Yanlei, L. Le, Y. Pengfei, Traveling wave fault location method analysis and prospect, 2020 5th Asia Conference on Power and Electrical Engineering (ACPEE), 1419–1423 (June 2020) [Google Scholar]

[2] L. Xun, L. Shun-gui, H. Rong-hui, A. Jingwen, A. Yunzhu, C. Ping, X. Zhengxiang, Study on accuracy traveling wave fault location method of overhead line - Cable hybrid line and itsinfluencing factors, 2017 Chinese Automation Congress (CAC), 4593–4597 (October 2017) [Google Scholar]

[3] J. Huibin, An Improved Traveling-Wave-Based Fault Location Method with Compensating the Dispersion Effect of Traveling Wave in Wavelet Domain, Mathematical Problems in Engineering, 1 (2017) [Google Scholar]

[4] R.G. Khuziashev, I.L. Kuzmin, V.D. Vasiliev, S.M. Tukaev, Practical implementation of the wave method for determining the location of damage in branched distribution electrical networks 6 (10) kV, Electricity, Transmission and distribution, 98–107 (2019) [Google Scholar]

[5] A.L. Kulikov, A.A. Loskutov, P.S. Pelevin, Algorithm for identification of a damaged section on cable and overhead power lines based on recognition of wave portraits, Electricity, 3, 11–17 (2018) [Google Scholar]

[6] V. Viculin, Automatic Feature Extraction for Signals Classification, XIX International Conference of Data Analytics and Management in Data Intensive Domains, 15–20 (October 2017) [Google Scholar]

[7] K. Sutha, J. Tamilselvi, A review of feature selection algorithms for data mining techniques, International Journal on Computer Science and Engineering, 7 (6), 63–67 (2015) [Google Scholar]

[8] B.U. Kohler, C. Hennig, R. Orglmeister, The principles of software qrs detection, IEEE Engineering in Medicine and Biology Magazine, 21 (1), 42–57 (January 2002) [Google Scholar]

[9] C. Abadie, T. Billard, T. Lebey, Numerical signal processing methods for partialdischarge detection in more electrical aircraft, 2016 IEEE International Conference on Dielectrics (ICD), 1, 540–543 (July 2016) [Google Scholar]

[10] N.T.H. Anh, T.H. Hoang, D.T. Dung, V.T. Thang, T.T.Q. Bui, An artificial neural network approach for electroencephalographic signal classification towards brain-computer interface implementation, 2016 IEEE RIVF International Conference on Computing Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), 205–210 (2016) [Google Scholar]

[11] T. Debnath, M.M. Hasan, T. Biswas, Analysis of ecg signal and classification of heart abnormalities using artificial neural network, 2016 9th International Conference on Electrical and Computer Engineering (ICECE), 353–356 (2016) [Google Scholar]

[12] You-Jin Park, Shu-Kai S. Fan, Chia-Yu Hsu, A Review on Fault Detection and Process Diagnostics in Industrial Processe, Processes, 8 (9), 1123 (9 September 2020) DOI: 10.3390/pr8091123 [Google Scholar]