基于Tsallis小波熵的变压器励磁涌流识别方法

针对变压器励磁涌流和内部故障电流识别的热点问题，为了有效克服香农熵在对信号的小波分解系数进行特征提取时具有局限性的缺陷，达到提高识别的有效性和快速性的目的。提出了基于Tsallis小波能量熵和时间熵判据的变压器励磁涌流和内部故障电流的识别新方法。该方法将小波分析与Tsallis熵结合对变压器暂态信号进行分析，在小波能量谱的基础上，得到Tsallis小波能量熵判据，并根据时间熵的定义得到Tsallis小波时间熵判据，综合利用两种判据对暂态信号进行识别。该方法不仅可以成功识别励磁涌流，并且提高了识别的准确性、可靠性和灵敏性。MATLAB/Simulink仿真实验结果验证了所提方法的有效性和准确性。

New approach of transformer inrush detected based on Tsallis wavelet entropy

The recognition of transformer magnetizing inrush and internal fault current is a hot issue. In order to solve the defects with the limitations of the Shannon entropy coefficients in wavelet decomposition of signal feature extraction, and improve the validity and speediness of recognition, a new method is presented to identify inrush current based on Tsallis wavelet energy entropy and Tsallis wavelet time entropy. The method analyses the transient signals of transformer by using wavelet analysis and Tsallis entropy. Based on wavelet energy spectrum and the definition of time entropy, Tsallis wavelet energy entropy criterion and Tsallis wavelet time entropy criterion are obtained to distinguished excitation inrush current from fault one. The method improves the accuracy of excitation inrush current recognition, reliability and sensitivity. The MATLAB/Simulink simulation results show the correctness and effectiveness of the proposed method.