Grounding Grids Fault Diagnosis Based on PCA-BP Neural Network

Abstract:

In order to solve the problem of needing to build accurate mathematical model in grounding grids fault diagnosis ,a new method for applying to the study of grounding grids fault diagnosis,which was based on BP neural network and improved by PCA theory，was introduced.The PCA(Principal component analysis)method was incorporated into the network,which not only solved the linear correlation of the input, but also simplified the network structure and reduced the parameters of neural net input. It realized optimum compression of fault sample data and enhanced classification speed and precision. The experimental results demonstrated that the method could decrease the number of the network input nerve cells effectively, and enhanced study efficiency and diagnosis accuracy. The way had very good fault distinguishing ability and vast prospect.

• Periodical: Advanced Materials Research (Volumes 516-517)
• Pages: 1774-1778
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.516-517.1774
• Online since: May 2012
• Authors: Xian Feng Liu, Min Fang Peng
• Keywords: Fault Diagnosis, Grounding Grid, Neural Network (NN), Principal Component Analysis (PCA)

References

[1] LIU Jian,et al. Grounding Grids Corrosion Diagnosis Based on Hierarchical Simplification of Network Topology[J]. Proceedings of the CSEE, 2008, 28(16): 122-128.

[2] ZHANG Xiu-li,et al. Development and Application Of Electrochemical Detection System for Grounding Grid Corrosion State[J]. Proceedings of the CSEE, 2008，28(19)：152—156.

[3] LIU Yang, CUI Xiang, ZHA0 Zhi-bin. Method for Diagnosing the Conductor Broken Point of Grounding Grids in Substation Based on Measuring the Magnetic Induction Intensity[J]. High Voltage Engineering, 2008, 34(7)：1389-1394.

[4] LIU Yang，CUI Xiang，LUTie-bing. ADiagnosis Method of Breaking Points in Grounding Grid for Substations[J]. Power System Technology, 2008，32(2)：21—25.

[5] Lv Ning, Liu Shao-bo, Yu Xiaoyang. Sensor Fault Diagnosis and reconstruction Based on the statistics of the PCAspace[D]. Harbin, Harbin University of Science and Technology,2008.

[6] Li Er-guo, Yu Jin-shou. Application on Process Fault Detection and Diagnosis Using PCA[R]. Shang Hai, East China university of science and technology, automation, 2001.

[7] Wayliang, Li Baiquan, Zhang 1ie. PCA—BP Neural Network in Gear Fault Diagnosis and Analysis[J]. Zhen Jiang: Jiang Su University，2005.

[8] Wang Wei, Wang Rong-jie, Hu Qing. Fault Diagnosis Technology Based on PCA and Neural Network[D],Guang Zhou, Guangdong University of Technology,2008.