WANG Zhi-wen, GONG Mao-fa, AN Bin, LI Lan-bing, LIU Tao
(College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China）
Abstract： The paper expounds importance of the transformer protection and analyzes the magnetizing inrush current produce condition. A comprehensive prevention based on the wavelet neural network methods for identification the magnetizing inrush current is put forward in this paper. Based on a lot of simulations, it can be drawn that sympathetic inrush waveform has no obvious difference with that of the inrush current. According to the characteristics of wavelet neural networks' huge computation and high sampling rate, a new method based on FPGA of a high-speed hardware platform is proposed to realise the algorithm. Utilizing technologies of wavelet neural networks and FPGA, the accuracy and real-time data processing speed of the protection device can be more effective. In a word, the research has high theoretical and practical value in the further improvement of transformer protection.
Key words： wavelet neural network； transformer protection； FPGA； DSP
CLD number： TM403.5 Document code： A
Article ID： 1674-8042(2014)02-0076-04 doi： 10.3969/j.issn.1674-8042.2014.02.015
References
[1] ZHANG Xiao-ming. Study on a new method for digital transformer protection based on wavelet neuram network and FPGA. Qingdao: Shandong University of Science and Technology, 2011.
[2] ZHANG Xiao, ZHANG Jian-wen. Research of magnetizing inrush current and fault current identification based on wavelet transform in transformer protection. Electrical Measurement and Instrumentation, 2012, 49(10): 73-77.
[3] LI Gui-cun, LIU Wan-shun. A algorithm of discrimination between inrush current and fault current of transformer based on self-correlation analysis. Automation of Electric Power Systems, 2001, 25(17): 25-28.
[4] ZHANG Xiu-chuan, HUANG Yi-zhuang. A fast algorithm of 2B-spline wavelet transform and its application to transformer protection. Relay, 2004, 32(1): 32-37.
[5] HU Xiao-peng, YI Li-gang. A new method of applying artificial neural network based on preprocess of wavelets to realize computer-based transformer protection. Relay, 2004, 32(6): 22-26.
[6] LIU Jian-li. Research on inrush current and its identification for transfomer. Zhenjiang: Jiangsu University, 2011.
[7] Masud S, McCanny J V. Reusable silicon Ip cores for diserete wavelet transform applications. IEEE Transactions on Circuits and Systems: Fundamental Theory and Applieations, 2004, 51(6): 1114-1124.
[8] Saied M M. A study on the inrush current phenomena in transformer substations. Proeeedings of Industry Applieations Conference & Thirty-sixth IAS Annual Meeting, IEEE, Piscataway (NJ), 2001, 2: 1180-1187.
[9] Bronzeado H S, Yacamini R. Phenomenon of sympathetic interaction between transformer scaused by inrush transients. IEEE Proceedings-Science, Measurement and Technology, 1995, 142(4): 323-329.
基于小波神经网络和FPGA的变压器保护装置的研究王志文, 公茂法, 安彬, 李岚冰, 刘涛
(山东科技大学 电气与自动化工程学院， 山东 青岛 266590)
摘要:本文在阐述变压器保护的重要性和分析励磁涌流产生条件的基础上， 提出了一种基于小波神经网络算法识别励磁涌流的方法， 大量的仿真结果表明， 和应涌流和励磁电流无明显的不同。 根据小波神经网络算法庞大的数据量和采样率， 提出基于FPGA硬件来实现相应的算法。 利用小波神经网络和FPGA技术， 可以提高保护装置的实时处理速度和精度， 该研究在进一步提高变压器的保护上具有较高的理论和实用价值。
关键词:小波神经网络； 电压器保护； FPGA； DSP
引用格式：WANG Zhi-wen, GONG Mao-fa, AN Bin, et al. Research on transformer protection based on wavelet neural network and FPGA. Journal of Measurement Science and Instrumentation, 2014, 5(2): 76-79. [doi: 10.3969/j.issn.1674-8042.2014.02.015]
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