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经验模态分解在EAST超导线圈电压信号分析中的应用 被引量:2

Application of EMD Algorithm to Analyze Voltage Signals of EAST Superconducting Coils
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摘要 超导托克马克聚变实验装置(EAST)的极向场超导线圈发生失超时,超导线圈电阻变化引起的微弱电压信号变化被强噪声淹没。针对该问题,运用快速傅里叶变化分析线圈电压信号的时频特性。根据分析结果,提出运用经验模态分解(EMD)方法对超导线圈两端电压信号进行分析,得到若干固有模态分量和余项,并得知微弱信号的能量主要分布在余项中。该方法能够消除环境影响,检测出电压信号的微弱变化。 In experimental advanced superconducting Tokamak (EAST) device, during the quench process of a superconducting coil, the voltage variation caused by change in coil resistance is so small that is submersed in the background noise. To solve the problem, the time-frequency characteristic of the coil voltage signals is analyzed by Fourier decomposition, then the coil voltage signals is decomposed into several intrinsic mode functions (IMF) and the remainder by empirical mode decomposition (EMD). The result shows that the energy of weak signal is distributed mainly in the remainder and the weak variation can be extracted from the noise by EMD.
出处 《电气工程学报》 2018年第2期9-14,共6页 Journal of Electrical Engineering
关键词 微弱信号 经验模态分解 线圈电压 趋势项 Weak signal, empirical mode decomposition, coil voltage, the remainder
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