Abstract
Power transformer insulation fault location is the key to improve the stability of power transformer. A Bayesian network based on power transformer insulation fault on-line monitoring method is proposed. The Bayesian network characteristic decomposition model is used to detect the insulation fault of power transformer, the high-resolution spectrum characteristic quantity of insulation fault of power transformer is extracted, the load balance analysis is carried out according to the output voltage and load difference of power transformer, the Bayesian network detection model of insulation fault of power transformer is constructed. Combined with PCI integrated information processor and relay transmission node network topology model, the on-line monitoring system design of power transformer insulation failure is realized. The simulation results show that the fault location of power transformer insulation is accurate and the visual resolution of fault is strong.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ma, M., Wang, J., Wang, Z., Li, P., Xiong, L.: Transient stability analysis of the global phase portraits in multi-machine system. Proc. CSEE 39(15), 4385–4394 (2019)
Wei, S., Yang, M., Han, X., et al.: Online identification for transient angle stability based on MLE index of phase trajectory. Autom. Electr. Power Syst. 41(16), 71–79 (2017)
Zhou, Y., Wu, J., Yu, Z., et al.: Power system transient stability assessment based on cluster features of rotor angle trajectories. Power Syst. Technol. 40(5), 1482–1487 (2016)
MartÃnez, Y.P., Vidal, C.: Classification of global phase portraits and bifurcation diagrams of Hamiltonian systems with rational potential. J. Differ. Equ. 261(11), 5923–5948 (2016)
Wang, T., Chiang, H.D.: On the number of unstable equilibrium points on spatially-periodic stability boundary. IEEE Trans. Autom. Control 61(9), 2553–2558 (2016)
Meng, H., Xu, H., Song, X.: Transformer fault diagnosis based on attribute reduction of rough set and SVM. Nanjing Hangkong Hangtian Daxue Xuebao/J. Nanjing Univ. Aeronaut. Astronaut. 49(4), 504–510 (2017)
Arani, M.F.M., Mohamed, Y.A.R.I.: Analysis and performance enhancement of vector-controlled VSC in HVDC links connected to very weak grids. IEEE Trans. Power Syst. 32(1), 684–693 (2017)
Zhang, H., Chen, W.: Tungsten oxide (WO3) micro balls composed of curly nanosheets for transformer fault gas diagnosis. J. Nanoelectron. Optoelectron. 12(12), 1305–1308 (2017)
Guo, C., Liu, W., Zhao, C., Ni, X.: Small-signal dynamics and control parameters optimization of hybrid multi-infeed HVDC system. Int. J. Electr. Power Energy Syst. 98, 409–418 (2018)
Ni, X., Gole, A.M., Zhao, C., Guo, C., et al.: An improved measure of AC system strength for performance analysis of multi-infeed HVDC systems including VSC and LCC converters. IEEE Trans. Power Delivery 33(1), 169–178 (2018)
Sreewirote, B., Ngaopitakkul, A.: Analysis on behaviour of wavelet coefficient during fault occurrence in transformer. IOP Conf. Ser. Earth Environ. Sci. 127(1), 012008 (2018)
Lim, K., Bastawrous, H.A., Duong, V.H., et al.: Fading Kalman filter-based real-time state of charge estimation in LiFePO4, battery-powered electric vehicles. Appl. Energy 169, 40–48 (2016)
Di, B., Zhou, R., Dong, Z.N.: Cooperative localization and tracking of multiple targets with the communication-aware unmanned aerial vehicle system. Control Dec. 31(04), 616–622 (2016)
Mejia-Barron, A., Valtierra-Rodriguez, M., Granados-Lieberman, D., et al.: Experimental data-based transient-stationary current model for inter-turn fault diagnostics in a transformer. Electr. Power Syst. Res. 152, 306–315 (2017)
Majchrzak, V., Parent, G., Brudny, J.-F.: Influence of the electrical circuit configurations of a DVR coupling transformer with a magnetic bypass. COMPEL Int. J. Comput. Math. Electr. Electron. Eng. 36(3), 804–810 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Chen, Yh., Tan, Ll., Liu, Yh. (2020). On-line Monitoring Method of Power Transformer Insulation Fault Based on Bayesian Network. In: Zhang, YD., Wang, SH., Liu, S. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 326. Springer, Cham. https://doi.org/10.1007/978-3-030-51100-5_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-51100-5_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-51099-2
Online ISBN: 978-3-030-51100-5
eBook Packages: Computer ScienceComputer Science (R0)