Research of Bus Protection Based on Support Vector Machine with Parameter Optimization

Han Han; Hou Jun Wang; Cheng Ti Huang; Bin Long

doi:10.4028/www.scientific.net/AMR.271-273.823

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 271-273Research of Bus Protection Based on Support Vector...

Research of Bus Protection Based on Support Vector Machine with Parameter Optimization

Abstract:

Bus reliability is essential for maintaining the steady state of electrical network in power plants and power transformer stations. Researches on techniques and methods to achieve highly reliable and intelligent bus protection are very important. A new bus protection method based on parameter optimization of the support vector machine (SVM) is provided in this study. Various types of fault data were collected and used as samples for sorting comparison and analysis of different bus faults. Data were obtained by comparing the SVM method with the artificial neural network (ANN) method. The results suggest that the SVM model using RBF kernel can better distinguish among bus normal operation and different bus fault types than the ANN model, and meet the precision requirements for bus protection.

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Periodical:

Advanced Materials Research (Volumes 271-273)

Pages:

823-828

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.271-273.823

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Online since:

July 2011

Authors:

Han Han, Hou Jun Wang, Cheng Ti Huang, Bin Long

Keywords:

Bus Protection, Fault Diagnosis, Parameter Optimization, Support Vector Machine (SVM)

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References

[1] Jian Luo, Yajun Li, Guoyu Xu. Reserch of Bus Protection Based on ANN Model. Automation of Electric Power Systems. 2002, 26(11): 41-44.

Google Scholar

[2] Xiucheng Dong; Han Han; Mei Shu; Research of Bus Protection Based on Artificial Neural Network, Intelligent Control and Automation, 2008. WCICA 2008. Page(s): 4261 - 4264.

DOI: 10.1109/wcica.2008.4593607

Google Scholar

[3] Vapnik: Statistical Learning Theory . (Publishing house of electronics industry. 2004. ).

Google Scholar

[4] Ravikumar, B.; Thukaram, D.; Khincha, H.P.; Comparison of Multiclass SVM Classification Methods to Use in a Supportive System for Distance Relay Coordination . Power Delivery, IEEE Transactions on 2010 , Page(s): 1296 – 1305.

DOI: 10.1109/tpwrd.2010.2041795

Google Scholar

[5] Thukaram, D.; Khincha, H.P.; Ravikumar, B.; An Intelligent Approach Using Support Vector Machines for Monitoring and Identification of Faults on Transmission Systems . Power India Conference, 2006 IEEE. 2006 , Page(s): 184 - 190.

DOI: 10.1109/poweri.2006.1632511

Google Scholar

[6] R. Salat and S. Osowski, Accurate Fault Location in the Power Transmission Line Using Support Vector Machine Approach, IEEE Trans. Power Systems, 2004 Page(s). 979–986.

DOI: 10.1109/tpwrs.2004.825883

Google Scholar