The overvoltage is the main cause of insulation damage in power grid

it has an important impact on the insulation reliability of electrical equipment

system insulation coordination

relay protection and operation control. It is of great practical significance to study the feature extraction and classification of the measured overvoltage in the distribution network for tracing the source of power grid operation accidents and risk assessment of equipment insulation

etc. This paper constructed the band of time-frequency distribution of overvoltage in Lo Shu Square based on FSWT and completed the overall and detail information of overvoltage extraction. The measured overvoltage feature automatically extraction and classification is achieved based on modified SSAEs. The influence of key parameters

namely

the size of convolutional patches

the number of convolutional maps and sparsity parameter in modified SSAEs are analyzed respectively

and the best optimization parameters are determined

the best classification and recognition effect is achieved. The results show that the combination of the Lo Shu Square of the time-frequency distribution of overvoltage and the modified SSAEs algorithm can automatically extract and classify the measured overvoltage waveform with good classification accuracy.