Research on Fault Diagnosis of High-Voltage Circuit Breaker in Electrical Power System Based on KPCA

Yi Ping Wang; Fei Mei; Jian Yong Zheng; Jun Mei

doi:10.4028/www.scientific.net/AMR.676.269

Paper Titles

Minimum Amplitude of Geomagnetic Disturbance in Electrical Power System Leading to 500kV transformer DC Bias
p.251

EHV Generator Loss of Field Protection Research in Electrical Power System
p.255

Development of Transformer Winding Distortion Testing Device in Electrical Power System Based on Electrical Properties
p.260

Double-Loop Control of Inverter with LCL Filter in Electrical Power System Based on Improved Instantaneous Symmetrical Component
p.265

Research on Fault Diagnosis of High-Voltage Circuit Breaker in Electrical Power System Based on KPCA
p.269

Research on LCR Filter in the Link of Shunt Active Power Filter in Electrical Power System
p.273

Research of Grid-Connected Photovoltaic System in Electrical Engineering with Energy Storage Based on DC Bus
p.277

Influence of the Arc Suppression Coil with Parallel Resistance on Single-Phase Arc Grounding Overvoltage
p.284

Study on Electromechanical Coupling Vibration of Simulation Technology Based on the Drive System
p.289

HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 676Research on Fault Diagnosis of High-Voltage...

Research on Fault Diagnosis of High-Voltage Circuit Breaker in Electrical Power System Based on KPCA

Abstract:

High-voltage circuit breakers (HVCBs) play an essential role in electrical power systems, which can ensure and control the smooth operation of power grids. Therefore, a fault diagnosis method of HVCBs based on Kernel Principal Component Analysis (KPCA) is proposed in this paper. As the fault data of HVCBs have the characteristic of multi-dimensional nonlinearity, the proposed method calculates inner kernel function of HVCBs' closing current in the original data space so as to achieve nonlinear mapping to the feature space of the original data. Afterwards, feature extraction and pattern classification of fault data can be accomplished in the feature space by means of monitoring SPE statistics. Experiment results have proved that the KPCA method can effectively improve the precision of fault diagnosis of HVCBs.

You might also be interested in these eBooks

Advanced Research on Material Engineering and Electrical Engineering

View Preview

Info:

Periodical:

Advanced Materials Research (Volume 676)

Pages:

269-272

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.676.269

Citation:

Cite this paper

Online since:

March 2013

Authors:

Yi Ping Wang, Fei Mei, Jian Yong Zheng, Jun Mei

Keywords:

Electrical Power System, Fault Diagnosis, High-Voltage Circuit Breakers (HVCBs), Kernel Principal Component Analysis (KPCA)

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Rakotomamonjy, "Variable selection using SVM-based criteria," Journal of Machine Learning Research, pp.1357-1370, April (2003).

Google Scholar

[2] J.-H. Cho, J.-M. Lee, S. W. Choi, D. Lee, and I.-B. Lee, "Fault identification for process monitoring using kernel principal component analysis," Chem. Eng. Sci., vol. 60, no. 1, p.279–288, January (2005).

DOI: 10.1016/j.ces.2004.08.007

Google Scholar

[3] Choi, S.W. and I. Lee, "Nonlinear dynamic process monitoring based on dynamic kernel PCA," Chemical Engineering Science, vol.59, pp.5897-5908, January (2004).

DOI: 10.1016/j.ces.2004.07.019

Google Scholar

[4] J. Cho, J. Lee and S.Wook choi, "Fault identification for process monitoring using kernel principal component analysis," Chemical Engineering Science, vol. 60, pp.279-288, January (2005).

DOI: 10.1016/j.ces.2004.08.007

Google Scholar

[5] G. Matteo, "Design of effective kernels for spectroscopy and molecular transport: Time-dependent current-density-functional theory," Journal of Chemical Physics, vol 134, pp.1-5, August(2011).

DOI: 10.1063/1.3558738

Google Scholar