The recognition of partial discharge mode is an important indicator of the insulation condition in transformers, based on which maintenance can be arranged. Discharge feature extraction is the key to recognize discharge mode. To solve the problem of poor stability and low recognition rate of partial discharge mode, this paper proposes a feature extraction method based on synchrosqueezed windowed Fourier transform and multi-scale dispersion entropy. First, the four partial discharge signals collected under laboratory conditions are decomposed by synchrosqueezed windowed Fourier transform, then a number of band-limited intrinsic mode type functions are obtained, and the original feature quantities of partial discharge signals are obtained by calculating the multi-scale dispersion entropies of each intrinsic mode type function. Based on that, original feature quantity is optimized by using the maximum relevance and minimum redundancy criteria. Finally, the classification is implemented by the support vector machine. Experimental results show that in the case of noise interference, the proposed synchrosqueezed windowed Fourier transform–multi-scale dispersion entropy method can still accurately describe the feature of different discharge signals and has a higher recognition rate than both the empirical mode decomposition–multi-scale dispersion entropy method and the direct multi-scale dispersion entropy method.

中文翻译：

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基于同步压缩加窗傅里叶变换和多尺度色散熵的局部放电特征提取

局部放电模式的识别是变压器绝缘状况的重要指标，可据此安排检修。放电特征提取是识别放电模式的关键。针对局放模式稳定性差、识别率低的问题，提出一种基于同步压缩加窗傅里叶变换和多尺度色散熵的特征提取方法。首先对实验室条件下采集的4个局部放电信号进行同步压缩加窗傅里叶变换分解，得到若干带限本征模型函数，通过多尺度计算得到局部放电信号的原始特征量。每个本征模式类型函数的色散熵。基于此，使用最大相关性和最小冗余标准优化原始特征量。最后，分类由支持向量机实现。实验结果表明，在噪声干扰的情况下，所提出的同步压缩加窗傅里叶变换-多尺度色散熵方法仍然可以准确描述不同放电信号的特征，并且比经验模态分解-多尺度方法具有更高的识别率。色散熵方法和直接多尺度色散熵方法。