摘要
针对传统方法处理局部放电信号时存在振荡明显、消噪不彻底等问题,采用基于自适应白噪声完备集成经验模态分解(complete ensemble empirical model decomposition with adaptive noise,CEEMDAN)与可调品质因子小波变换(tunable Q⁃factor wavelet transform,TQWT)相结合的方法对局部放电信号进行消噪处理。采用CEEMDAN将含噪变压器局部放电信号分解成多个固有模态函数(intrinsic mode function,IMF)分量,并利用相关系数判断IMF分量与原始信号的相关度。将弱相关者视为劣质IMF,对其进行TQWT分解,利用能量占比与峭度指标来筛选小波子带,提取IMF的有效细节信息,进行TQWT逆变换,从而得到新的IMF分量;将强相关者视为优质IMF,与变换后的新IMF分量共同进行信号重构,得到消噪结果。仿真及实测信号分析验证了该方法的有效性和实用性,相比传统的经验模态分解(empirical mode decomposition,EMD)方法,仿真信号经所提方法去噪后的波形失真百分比下降了44.94%;相较于仅使用CEEMDAN,现场信号经所提方法去噪后的噪声抑制比提高了26.64%。
In response to the phenomena of significant oscillations and incomplete noise reduction when dealing with partial discharge signals using traditional methods,a combined approach based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)and tunable Q-factor wavelet transform(TQWT)is adopted to denoise the PD signals.Firstly,the CEEMDAN is employed to decompose the noisy transformer PD signals into multiple intrinsic mode functions(IMF),and the correlation coefficient is utilized to assess the correlation between the IMF components and the original signal.Those with weak correlations are considered inferior IMFs.They are decomposed using TQWT.Energy proportion and kurtosis indicators are utilized to select wavelet sub-bands,extracting effective detailed information from the IMF.Subsequently,inverse transformation of the TQWT is applied to obtain new IMF components.The IMFs with strong correlations are considered high-quality.They are reconstructed together with the transformed new IMF components to obtain the denoising result.Simulation and field signal analysis verify the effectiveness and practicability of the proposed method.Compared to the traditional empirical mode decomposition(EMD)method,the percentage of waveform distortion decreased by 44.94%after denoising simulated signals using the proposed method.Compared to using only CEEMDAN,the noise suppression ratio increases by 26.64%after denoising on-site signals using the proposed method.
作者
尚海昆
张冉喆
黄涛
林伟
赵子璇
SHANG Haikun;ZHANG Ranzhe;HUANG Tao;LIN Wei;ZHAO Zixuan(Key Laboratory of Modern Power System Simulation&Control and Renewable Energy Technology,Ministry of Education,Northeast Electric Power University,Jilin 132012,China)
出处
《电力科学与技术学报》
CAS
CSCD
北大核心
2024年第1期272-284,共13页
Journal of Electric Power Science And Technology
基金
国家自然科学基金(51877031)。
