The Fault Diagnosis of Electric Power Metering System Based on Momentum BP Neural Network

Abstract:

Momentum BP neural network is used to diagnose the fault of high voltage electric power metering system according to the advantage of strong self-adaptation. First of all, the information of 7 operation parameters of metering system is extracted as input values of the neural network, secondly, we collect samples to train and test BP network and use 4-bit binary number to represent the normal circumstance and 7 main fault types, The results show that the momentum BP network model is effective in the fault diagnosis of high voltage electric power metering system.

• Periodical: Applied Mechanics and Materials (Volumes 668-669)
• Pages: 724-728
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.668-669.724
• Online since: October 2014
• Authors: Yue Yang Zhang*, Jian Jun Zhao, Wei Liang
• Keywords: Artificial Neural Network (ANN), Fault Diagnosis, High Voltage Electric Power Metering System, Momentum BP Algorithm

References

[1] Jianjun Zhao, Sujun Zhang. Fault Analysis and Modeling for the High Voltage Electric Power Metering System[J]. Electrical Measurement & Instrumentation, 2007, 44(4): 5-8.

[2] Chunbin Qin, Jianjun Zhao, Lei Zhang. Analyzing and Modeling for Shunt Current Electric Larceny of Electric Power Metering System. Electrical Measurement & Instrumentation, 2009, 46(2): 39-42.

[3] Jinbao Yang, Changhong Zhang, Ping Chen. Network Fault Diagnosis Based on Improved BP Neural Network [J]. Computer & Digital Engineering, 2012, 40(2): 65-67.

[4] Yu Wang, Qiyi Guo, Weigang Li. Predictive Model Based On Improved BP Neural Network and It's Application [J]. Computer Measurement & Control, 2005, 13(1): 39-42.

[5] Li Ma, Cixiong Xu. Detection Method of Laser Gyroscope Cavity Adjustment Based On Momentum BP Neural Network [J]. Chinese Journal of Lasers, 2012, 39(4): 1-8.

DOI: 10.3788/cjl201239.0402007

[6] Songjie Tang, Jianwei Lu, Pu Zhou. Research on Determining the Protecting Focal Project Based on Momentum BP-NN [J]. Command Control & Simulation, 2006, 28(5): 81-85.

[7] Lina Xu, Neural Networks Control [M]. Beijing: Publishing House of Electronics Industry, (2003).

[8] Hongyuan Wang, Yudong Shi. Artificial neural network technology and its application [M]. Beijing: China Petrochemical Press, (2003).

[9] Shuchen Li, Xianda Feng, Shucai Li. The Normalization Process of the Multi-field Information from A Coal Mine Water-inrush Model Test. Journal of China Coal Society, 2011, 36(3): 447-451.

[10] Defeng Zhang. The application design of MATLAB neural network. Beijing: China Machine Press, (2012).