Post-wavelet analysis treating method and device for electric power transient signal

Abstract

The invention discloses a wavelet analysis post-processing method and device for a power transient signal. The method is to conduct the following one or more post-processing on the wavelet coefficients after the power transient signal is analyzed by wavelet, and the result is to send power Monitoring center processing: modulus maximum extraction, singularity detection, obtaining modulus maximum points and singularity; statistical processing or cluster analysis of wavelet coefficients; neural network classification, that is, wavelet coefficients or wavelet post-processed data as neural Network input, output neural network classification results; energy analysis, to obtain energy distribution coefficient and energy fluctuation coefficient; wavelet entropy calculation, to obtain wavelet energy spectrum entropy or wavelet time entropy or wavelet singular entropy or wavelet time-frequency entropy or wavelet average entropy or wavelet distance entropy. It can effectively extract the characteristics of power transient signals, which can be applied to power system transmission line traveling wave distance measurement and fault identification, power quality analysis and equipment fault diagnosis.

Description

technical field [0001] The invention relates to a wavelet analysis post-processing method and device for power transient signals. Background technique [0002] In power systems, transient signals can be generated due to faults, adverse power change events, or normal operation of power electronic switching devices. In order to grasp the characteristics of possible transient signals, their impact and hazards on the power system, and early diagnosis and prediction of the operation or failure of the power grid, transmission lines, and large-scale power equipment, carry out condition-based maintenance of power grids and power equipment, etc. , long-term monitoring of power transient signals is required, and the detected and recorded signals are processed and identified to provide users or power companies with an understanding of the power transients that occur; at the same time, large-scale electrical tests, such as high-voltage power equipment, Line switching, etc., also need t...

AI Technical Summary

Problems solved by technology

[0004] Wavelet transform can detect the transient state, but there are many coefficients after transformation. If they are all used as features, the performance of the classifier will be seriously reduced, and it is not suitable for real-time applications. Dimensionality reduction is required; It is also quite difficult to perform feature extraction or classification of signal components. From these two aspects alone, the wavelet transform post-processing method of transient signals is necessary

However, there is still no complete and systematic analysis and processing method and its special devices. The existing wavelet analysis instruments lack post-processing analysis and have single functions, so they cannot be directly used in power system transient analysis.

Images