基于EMD分解与压缩传感的泄漏电流压缩方法

Leakage Current Data Compression Method Based on EMD Analytical and Compressed Sensing

为了提高绝缘子监测系统数据传输和处理速度,对非平稳泄漏电流信号进行数据压缩至关重要。压缩传感理论(Compressed Sensing,CS)将采样与压缩合并进行,少量采样就能很好地恢复信号,不仅降低对硬件要求,而且提高压缩效率。然而CS恢复非平稳信号过程中计算复杂度很高。提出经验模态分解(EMD)和CS相结合的方法,将非平稳泄漏电流分解成有限个相对平稳的固有模态IMF,然后对相对平稳的IMF分量进行CS处理。实验结果表明,CS处理经EMD分解的泄漏电流信号不仅提高整个系统的处理速度和运行效率,而且提高了压缩比和重构精度。

In order to increase data transmission and processing speed in the insulator monitored system,it is essential to compress the non-stationary leakage current.Compressed Sensing（CS） com-bines sampling and compression with a small amount of sample to reconstruct signal well,which not only reduces hardware requirements but also improves compression efficiency.However,computational complexity is high in CS recovery process of non-stationary signals.Method that CS combined with empirical mode decomposition（EMD） is proposed.Non-stationary leakage current is decomposed into a finite number of stationary intrinsic mode functions（IMF）,then CS process is carried out on relatively smoothly IMF component.The experiment results indicate that it not only enhances the speed of process and operational efficiency but also improves compression ratio and reconstruction accuracy in the case that CS processes leakage current signal by EMD decomposition.