基于改进小波系数奇异值分解和小波去噪的低频振荡时变模式辨识

提出了一种基于连续小波变换(continuous walelet t r a n s f o r m , C W T )和奇异值分解( s i n g u l a r v a l u e decomposition,SVD)相结合的提升小波系数 SVD 辨识信号振荡频率和模式信息提取及信号去噪的新方法.克服了噪声较大或者密集模态时,小波脊线不清晰甚至会出现混叠和交叉难以提取频率的情况,根据提升的小波系数奇异值分解频率向量识别各阶振荡模式的频率.同时选用小波能量系数来识别主导振荡模式,用小波软阈值去噪和 SVD 分解后矩阵重构来进行信号去噪.CWT 可以处理含时变振荡模式的低频振荡信号,且对模式参数具有较高的辨识精度.仿真算例验证了算法的有效性和适用性

A Method to Identify Time-Varying Mode of Low Frequency Oscillation by Continuous Wavelet Transform Based on Raising Singular Value Decomposition of Wavelet Coefficient and Wavelet Denoising

Based on the combination of continuous wavelet transform(CWT) with singular value decomposition(SVD),a new algorithm to identify oscillation frequency of signal,extract mode information and denoise signal by raising SVD of wavelet coefficient is proposed.The condition that under high noise level or closely spaced mode of noise,the wavelet ridges are unsharp and even the frequency is hard to extract due to the aliasing and intersection of wavelet ridges can be overcome by the proposed method,and the frequencies of oscillation modes in different orders can be identified according to frequency vectors of the raised SVD of wavelet coefficients.Meanwhile the wavelet energy coefficient is chosen to identify the dominant oscillation mode,and signal denoising is performed by use of wavelet soft-thresholding denoising and restructured matrix after the SVD of wavelet coefficient.CWT can be used to deal with time-varying low-frequency oscillation signals containing time-varying oscillation mode,and the identification accuracy of mode parameters is high.Both effectiveness and applicability of the proposed algorithm are verified by simulation results.