Fault Locating of Grounding Grids Based on Extreme Learning Machine Elman Neural Network

Shu Di He; Min Fang Peng; Ke Xin Zhao; Ling Jian Li

doi:10.4028/www.scientific.net/AMR.787.954

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 787Fault Locating of Grounding Grids Based on Extreme...

Fault Locating of Grounding Grids Based on Extreme Learning Machine Elman Neural Network

Abstract:

In order to improve the accuracy and efficiency of the fault location in grounding grids, a new method combing Extreme Learning Machine (ELM) with neural network was proposed. The method compares the voltages of different node to determine whether the branch is fault or not. The simulation results show that this method can save time and improve accuracy.

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Periodical:

Advanced Materials Research (Volume 787)

Pages:

954-958

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.787.954

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Online since:

September 2013

Authors:

Shu Di He, Min Fang Peng, Ke Xin Zhao, Ling Jian Li

Keywords:

Extreme Learning Machine, Fault Location, Grounding Grid, Neural Network (NN)

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References

[1] F.P. Dawalibi, Electromagnetic fields generated by overhead and buried short conductors Part 2-Ground conductor, IEEE trans. Power Delivery, 1986, 1(4): 112-119.

DOI: 10.1109/tpwrd.1986.4308037

Google Scholar

[2] Xiao Xinhua, Liu Hua, Chen Xianlu, Zhang Xiaoling. Analysis of grounding grid corrosion diagnosis and breakpoint theory [J]. Journal of Chongqing University, 2001. 1. 5.

Google Scholar

[3] Guo Yu. Study and instrument virtual, real component spectrum measuring methods of grounding electrode impedance of the preparation of [D]. master's degree thesis of Beijing University of Geoscience, (2006).

Google Scholar

[4] Liu qingzhen, Cai jinding, No injury detection method of Power System's grounding grids[J]. High electric voltage technique. 2003, 29（6）：47-49[In Chinese].

Google Scholar

[5] Zeng ren．Artificial neural network and its application [M]. Beijing: Tsinghua University press,. (1999).

Google Scholar

[6] Fu longhai, Wu guangning. genetic optimization neural network in Qinghai-Tibet grounding grids' design[J]. High electric voltage technique, 2006, 32(5): 90-92.

Google Scholar

[7] Yang Shiyuan. Simulation system fault diagnosis and reliability analysis of [M]. Tsing Hua University publishing, (1993).

Google Scholar

[8] He Zhiqiang, Wen Xi-shan, Pan Zhuohong. New algorithm [J]. for grounding grid corrosion of high voltage technology, 2007, No. 4: 83-86.

Google Scholar

[9] G. -B. Huang, L. Chen. Convex Incremental Extreme Learning Machine[J]. Neurocomputing , 2007, 70: 3056-3062.

DOI: 10.1016/j.neucom.2007.02.009

Google Scholar

[10] G. -B. Huang, Q. -Y. Zhu, C. -K. Siew. Extreme learning machine: theory and applications. [J]. Neurocomputing, 2006, 70(1-3): 489-501.

DOI: 10.1016/j.neucom.2005.12.126

Google Scholar

[11] Hong bingrong, Jin feihu,. Multilayer feedforward neural network based on ACA[J]. Journal of Harbin Engineering University, 2003, 35（7）：823-825．[In Chinese].

Google Scholar

[12] Duan hai-bin, The principle and application of ant colony algorithm[M]. Beijing: Science Publishing House, (2005).

Google Scholar

[13] G. -B. Huang, Q. -Y. Zhu, C. -K. Siew. Extreme learning machine: theory and applications. [J]. Neurocomputing, 2006, 70(1-3): 489-501.

DOI: 10.1016/j.neucom.2005.12.126

Google Scholar

[14] Zeng Rong. Researches on grounding technologies for substations and power plants in high soil resistivity areas[D]. Beijing; Tsing Hua University Ph. D Thesis, (2000).

Google Scholar