摘要
光纤电流互感器(FOCT)通过将光能转换为磁场能再转换回光能的原理,已逐渐成为配电网规划中输电线路电流检测和监测的关键组件。然而,由于FOCT的工作环境复杂多变,其输出信号中往往包含各类噪声,对设备的长期运行稳定性和检测精度造成一定影响。针对FOCT输出信号的特性,在现有小波包阈值去噪技术基础上,提出了一种结合完全集成经验模态分解与自适应噪声(CEEMDAN)和小波包阈值去噪的联合算法。该算法旨在通过CEEMDAN算法对复杂的非线性、非平稳信号进行高效分解,再利用小波包阈值去噪技术精确去除噪声成分,从而有效提升信号处理的精度和效率。该算法首先采用CEEMDAN算法对初始信号进行分解,得到包含不同频率成分信息的一系列固有模态函数(IMF);其次,针对每个IMF应用小波包阈值去噪技术,根据信号特征自适应选择最优的小波基和阈值参数,实现对噪声的有效抑制;最后,将去噪后的IMF重构,恢复出更纯净、准确的原始信号。试验结果分析表明,该算法在处理特定类型噪声时可显著改善信噪比,对部分信号的信噪比提升幅度可达20 dB以上。这表明该算法可有效抑制噪声,保留信号细节,并具有信号分析更全面、自适应性更强等优势,从而提高FOCT的运行稳定性与检测精度。
Optical fiber current transformers(FOCTs)have gradually become key components for current detection and monitoring in transmission lines in distribution network planning,based on the principle of converting light energy to magnetic field energy and back to light energy.However,due to the complex and variable working environment of FOCTs,their output signals often contain various types of noise,which affects the long-term operational stability and detection accuracy of the equipment.In response to the characteristics of FOCT output signals,a combined algorithm integrating complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)and wavelet packet threshold denoising is proposed on the basis of the existing wavelet packet threshold denoising technology.This algorithm aims to efficiently decompose complex nonlinear and non-stationary signals through the CEEMDAN algorithm,and then precisely remove noise components using wavelet packet threshold denoising technology,thereby effectively improving the accuracy and efficiency of signal processing.The algorithm first uses the CEEMDAN algorithm to decompose the initial signal,obtaining a series of intrinsic mode functions(IMFs)containing information of different frequency components;then,for each IMF,the wavelet packet threshold denoising technology is applied,and the optimal wavelet basis and threshold parameters are adaptively selected based on signal characteristics to effectively suppress noise;finally,the denoised IMFs are reconstructed to restore a cleaner and more accurate original signal.The analysis of experimental results shows that this algorithm can significantly improve the signal-to-noise ratio when dealing with specific types of noise,with the signal-to-noise ratio of some signals increasing by more than 20 dB.This indicates that the algorithm can effectively suppress noise,retain signal details,and offer advantages such as more comprehensive signal analysis and adaptability,thereby enhancing the operational stability and detection accuracy of FOCTs.
作者
孟庆喜
岳光华
赵新科
王炳蔚
董华军
MENG Qingxi;YUE Guanghua;ZHAO Xinke;WANG Bingwei;DONG Huajun(Henan Pinggao Electric Co.,Ltd.,Pingdingshan 467000,China;School of Rail Intelligence Engineering,Dalian Jiaotong University,Dalian 116028,China;School of Mechanical Engineering,Dalian Jiaotong University,Dalian 116028,China)
出处
《大连交通大学学报》
2025年第6期157-164,共8页
Journal of Dalian Jiaotong University
基金
辽宁省自然科学基金(2024-BS-200)
辽宁省属本科高校基本科研业务费专项资金(LJ212410150060)。
关键词
光纤电流互感器
CEEMDAN算法
小波包阈值去噪
fiber-optical current transformer
CEEMDAN algorithm
wavelet packet threshold denoising
