Judgment method of ultra-high voltage equipment local discharge detection data

Abstract

The invention discloses a judgment method of ultra-high voltage equipment local discharge detection data. The judgment method comprises the following steps: sampling a continuous ultrasonic frequencysignal to reduce to the continuous sound wave frequency signal capable of being heard by the human ear; continuously intercepting a frame sound wave frequency signal with a set time length; extractinga Mayer frequency cepstrum coefficient of the frame sound wave frequency signal as a to-be-identified fault discharge feature; sending the extracted to-be-identified fault discharge feature into a CNN convolution neural network, enabling the to-be-identified fault discharge feature to enter a fault classifier of a CNN convolution neural network output classification layer through CNN convolutionneural network analysis, wherein the CNN convolution neural network identifies the to-be-identified fault discharge feature and outputs the to-be-identified fault discharge feature according to the fault classifier formed by learning the known fault discharge feature in advance. The mode learning and identification are performed on the fault type by directly using the convolution neural network CNN, the identification accuracy rate is improved, and the manual intervention is reduced or avoided.

Description

technical field [0001] The invention relates to a fault diagnosis and discrimination method for electrical equipment, in particular to a discrimination method for partial discharge detection data of UHV equipment. Background technique [0002] As the normal operation of high-voltage switches and transformers in the power system is directly related to the reliable operation of the entire power system, timely detection of partial discharge of high-voltage switches and transformers can effectively prevent their failure. Long-term partial discharge accumulation will cause a series of physical and chemical reactions in high-voltage equipment, aggravate insulation damage, and cause equipment failure. Partial discharge state detection is an important means to ensure the reliable operation of high-voltage equipment, and partial discharge fault identification is the core link of partial discharge detection. [0003] Publication number CN105203936A discloses "a method for identifying...

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Problems solved by technology

[0003] Publication number CN105203936A discloses "a method for identifying the type of partial discharge defects in power cables based on spectrum analysis". This method compares and analyzes the partial discharge defect types by extracting the spectral characteristics of discharge defects and a database of defect type spectral characteristics established in advance. The process is to determine the type of partial discharge defect by comparing the similarity. Since the comparison of similarity is determined by the preset threshold, the accuracy of this method is uncertain, and when the similarity does not meet the requirements, manual intervention is required to determine the type of partial discharge defect , there is a problem of too many people intervening

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