An FPGA-based phase self-calibration system for micro-current sensor

被引：0

作者：

Chen, Gang ^{[1
]}

Chen, Xu ^{[1
]}

Chen, Tianxiang ^{[2
]}

Gong, Guoliang ^{[1
]}

Bian, Yi ^{[1
]}

Lu, Huaxiang ^{[1
]}

机构：

[1] Institute of Semiconductors, Chinese Academy of Sciences

[2] Department of Electronic and Electrical Engineering, Xiamen University of Technology

来源：

Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2013年 / 37卷 / 20期

关键词：

Dielectric loss; FastICA algorithm; Micro-current sensor; Phase difference; Self-calibration;

D O I：

10.7500/AEPS201211086

中图分类号：

学科分类号：

摘要：

The micro-current sensor is the key equipment for picking up the weak current signal. However, in practical applications such factors as temperature, humidity, strong electromagnetic interference, surge current, and run-time drift will change its phase characteristics, exerting a significant impact on phase-sensitive measurement, as in the case of the dielectric loss tan δ measurement for electrical equipment insulation online monitoring. To solve this problem, the FastICA algorithm is introduced in this paper. The field programmable gate array (FPGA) implementation of the phase self-calibration system for the micro-current sensor is proposed. The floating-point units are used in this implementation and the structure of the algorithm is optimized. The results show that the measuring precision, speed, and the fault-tolerant ability of the designed system meet engineering requirements. State Grid Electric Power Research Institute Press.

引用

页码：102 / 107

页数：5

共 11 条

[1]

Ma X., Lu H., Phase difference measurement method for current sensor based on ICA and BP network, Chinese Journal of Scientific Instrument, 29, 6, pp. 1314-1318, (2008)

[2]

(2012)

[3]

Sun H., Pu Z., Nie P., A new interference reduction method in dielectric loss tangent δ on-line monitoring for capacitive equipment, Automation of Electric Power Systems, 28, 3, pp. 67-70, (2004)

[4]

Li Z., Zeng X., Qin D., Et al., On-line measurement system of dielectric loss tangent for capacitive apparatus based on the injecting signal method, Automation of Electric Power Systems, 30, 16, pp. 57-60, (2006)

[5]

Chen T., Zhang B., Chen T., Et al., New on-line high precision tan δ monitoring system for capacitive equipment, Automation of Electric Power Systems, 28, 15, pp. 67-70, (2004)

[6]

Liu W., Li Y., Huang X., Et al., Application of BSS algorithm on FastICA in on-line monitoring system of relative dielectric loss, High Voltage Apparatus, 46, 2, pp. 86-90, (2010)

[7]

Comon P., Independent component analysis, a new concept, Signal Processing, 36, 3, pp. 287-314, (1994)

[8]

Amari S., Cichocki A., Yang H.H., A new learning algorithm for blind signal separation, Advances in Neural Information Processing Systems, pp. 757-763, (1996)

[9]

Hyvarinen A., Oja E., A fast fixed point algorithm for independent component analysis, Neural Computation, 9, 7, pp. 1483-1492, (1997)

[10]

Hyvarinen A., Fast and robust fixed-point algorithms for independent component analysis, IEEE Trans on Neural Networks, 10, 3, pp. 626-634, (1999)

← 1 2 →