一种基于小波变换的发电机缺陷分析方法

发电机是一个涉及多状态信息的复杂系统,对其进行缺陷分析时往往需要同时考虑多组状态量间的相互关联。现阶段,这一工作通常由人工实现,效率较低,且受主观因素影响较大。为此,本文提出一种基于连续小波变换的相关性分析方法,通过自动计算发电机多组状态量之间在不同时间范围和频域范围内的局部数据相关性,为进行发电机缺陷分析时所需的相关性信息提供有力支撑,并对所提出的算法进行了仿真验证。结果表明,所提方法可以实现状态量之间局部相关性的有效分析,分析结果具有良好的鲁棒性,可以有效的提升技术人员分析发电机缺陷的工作效率和准确性。

A Fault Analysis Method for Generator based on Wavelet Transformation

The generator is a complex system involving multiple status information.It is often necessary to consider the correlations between multiple status information at the same time when analyzing its defects.Nowadays,this work is usually implemented manually,which is low efficient and highly influenced by subjective factors.To solve these issues,this paper proposes a correlation analysis method based on continuous wavelet transformation.This method can automatically calculate the local correlation between multiple status information of the generator within different time ranges and frequency ranges and that can be available for the generator defect analysis.The proposed method is simulated and verified.The results show that the effectiveness and good robustness,which can effectively improve the technical staff's efficiency and accuracy in analyzing generator defects.