智能电网需要先进传感测量技术的支持。为此,针对智能电网中的电流测量需求,介绍了基于巨磁阻效应的高性能电流传感器,包括传感器系统结构、磁环、巨磁阻传感芯片、信号处理电路等的设计,以及对温度稳定性、电磁兼容性的设计。实验结果...智能电网需要先进传感测量技术的支持。为此,针对智能电网中的电流测量需求,介绍了基于巨磁阻效应的高性能电流传感器,包括传感器系统结构、磁环、巨磁阻传感芯片、信号处理电路等的设计,以及对温度稳定性、电磁兼容性的设计。实验结果表明,所设计的基于巨磁阻效应的高性能电流传感器在对电网中暂态电流、直流电流、泄漏电流、电晕电流的测量中表现出良好的性能,实现了对带宽直流到10 MHz、幅值1 m A至1.6 k A的电流的精确测量。相比其他电流测量装置,基于巨磁阻效应的高性能电流传感器具有体积小、灵敏度高、成本低、测量范围大、可集成度高等综合优势,适应了智能电网的测量需求。最后,提出了后续研究方向。展开更多
A grating eddy current displacement sensor(GECDS) can be used in a watertight electronic transducer to realize long range displacement or position measurement with high accuracy in difficult industry conditions.The pa...A grating eddy current displacement sensor(GECDS) can be used in a watertight electronic transducer to realize long range displacement or position measurement with high accuracy in difficult industry conditions.The parameters optimization of the sensor is essential for economic and efficient production.This paper proposes a method to combine an artificial neural network(ANN) and a genetic algorithm(GA) for the sensor parameters optimization.A neural network model is developed to map the complex relationship between design parameters and the nonlinearity error of the GECDS,and then a GA is used in the optimization process to determine the design parameter values,resulting in a desired minimal nonlinearity error of about 0.11%.The calculated nonlinearity error is 0.25%.These results show that the proposed method performs well for the parameters optimization of the GECDS.展开更多
文摘智能电网需要先进传感测量技术的支持。为此,针对智能电网中的电流测量需求,介绍了基于巨磁阻效应的高性能电流传感器,包括传感器系统结构、磁环、巨磁阻传感芯片、信号处理电路等的设计,以及对温度稳定性、电磁兼容性的设计。实验结果表明,所设计的基于巨磁阻效应的高性能电流传感器在对电网中暂态电流、直流电流、泄漏电流、电晕电流的测量中表现出良好的性能,实现了对带宽直流到10 MHz、幅值1 m A至1.6 k A的电流的精确测量。相比其他电流测量装置,基于巨磁阻效应的高性能电流传感器具有体积小、灵敏度高、成本低、测量范围大、可集成度高等综合优势,适应了智能电网的测量需求。最后,提出了后续研究方向。
文摘A grating eddy current displacement sensor(GECDS) can be used in a watertight electronic transducer to realize long range displacement or position measurement with high accuracy in difficult industry conditions.The parameters optimization of the sensor is essential for economic and efficient production.This paper proposes a method to combine an artificial neural network(ANN) and a genetic algorithm(GA) for the sensor parameters optimization.A neural network model is developed to map the complex relationship between design parameters and the nonlinearity error of the GECDS,and then a GA is used in the optimization process to determine the design parameter values,resulting in a desired minimal nonlinearity error of about 0.11%.The calculated nonlinearity error is 0.25%.These results show that the proposed method performs well for the parameters optimization of the GECDS.
