Due to limitations in the generalisation ability of currently proposed improved variational mode decomposition (VMD) methods, it is hard to precisely and efficiently discern signal characteristics from different power systems. Meanwhile, it is difficult to separate non-order noise sources in current studies. To address this issue, a novel scheme is proposed based on parameter-adaptive VMD and partial coherence analysis (PCA) for separating noise sources. In this approach, weighted fuzzy-distribution entropy (FuzzDistEn) is constructed to optimise the VMD to adaptively obtain the optimal parameters, considering the complexity of the signal system, and the mutual information between the decomposition components and the original signal. To verify the effectiveness and superiority of the proposed method, the paper respectively compares the decomposition results of the simulated signal using different objective functions, and shows that the weighted FuzzDistEn has a better decomposit...

中文翻译：

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基于加权模糊分布熵的噪声源分离参数自适应变模分解方法

由于当前提出的改进的变分模式分解（VMD）方法的泛化能力有限，因此难以准确有效地识别来自不同电源系统的信号特性。同时，在当前的研究中很难分离无序噪声源。为了解决这个问题，提出了一种基于参数自适应VMD和部分相干分析（PCA）的分离噪声源的新方案。在这种方法中，考虑到信号系统的复杂性以及分解分量与原始信号之间的相互信息，构造了加权模糊分布熵（FuzzDistEn）来优化VMD以自适应地获得最佳参数。为了验证所提方法的有效性和优越性，