Cable partial discharge mode identification method based on depth sample enhancement

A partial discharge and pattern recognition technology, applied in the direction of testing dielectric strength, etc., can solve the problems of cable running failure, bringing errors, a large amount of training data, etc., to achieve the effect of avoiding errors, high prediction and recognition accuracy, and simple conversion process

Active Publication Date: 2021-03-26
JIAOZUO POWER SUPPLY COMPANY OF STATE GRID HENAN ELECTRIC POWER
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Problems solved by technology

[0002] High-voltage cables are one of the main forms of power transmission, and are widely used in wind farms, solar energy and other distributed energy transmission and grid connection, as well as offshore oil and gas platforms, offshore island power transmission, etc. , cable body or accessories manufacturing quality, laying installation quality and other reasons caused by cable operation failure will cause different degrees of economic loss and social impact
[0003] The traditional cable partial discharge pattern recognition method relies heavily on the selection of manual features, and the design of manu

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  • Cable partial discharge mode identification method based on depth sample enhancement
  • Cable partial discharge mode identification method based on depth sample enhancement
  • Cable partial discharge mode identification method based on depth sample enhancement

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Example Embodiment

[0038] Example 1

[0039] Such as Figure 5 As shown, a partial discharge pattern identification method based on depth sample enhanced cable, including the following steps:

[0040] Step A: The partial discharge time domain signal of the collected cable insulation defect is subjected to the conversion of the image to obtain a two-dimensional image as an initial sample set;

[0041] When the signal is collected, the insulation defect is first boost until the defect breakdown is determined, and the breakdown voltage of the defect is determined, and then a certain number of discharge voltage values ​​are selected in the defect discharge voltage range, maintain the discharge voltage, and collect the local discharge current of DC cable. The domain signal, the time domain signal signals to the conversion of the image, follows the steps of: randomly select the time domain signal within one period of time from the acquired time domain signal, constitute the signal indicator value correspo...

Example Embodiment

[0054] Example 2

[0055] Such as Figure 5 As shown, a partial discharge pattern identification method based on depth sample enhanced cable, including the following steps:

[0056] Step A: The partial discharge time domain signal of the collected cable insulation defect is subjected to the conversion of the image to obtain a two-dimensional image as an initial sample set;

[0057] When the signal is collected, the insulation defect is first boost until the defect breakdown is determined, and the breakdown voltage of the defect is determined, and then a certain number of discharge voltage values ​​are selected in the defect discharge voltage range, maintain the discharge voltage, and collect the local discharge current of DC cable. The domain signal, the time domain signal signals to the conversion of the image, follows the steps of: randomly select the time domain signal within one period of time from the acquired time domain signal, constitute the signal indicator value correspo...

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Abstract

The invention relates to a cable partial discharge mode recognition method based on depth sample enhancement. The method comprises the following steps: converting a collected partial discharge time domain signal of a cable insulation defect into a two-dimensional image, taking the two-dimensional image as an initial sample set, inputting the two-dimensional image into a generative adversarial network, and carrying out the depth sample enhancement, adding the enhanced sample into the initial sample set to form a new sample set, constructing a convolutional neural network model, and performing supervised training on the convolutional neural network model by using the new sample set to obtain a trained convolutional neural network model; converting the time domain signal of the insulation discharge type to be predicted into a two-dimensional image, inputting the two-dimensional image into the trained convolutional neural network model, and outputting the predicted insulation defect discharge type. According to the method, errors caused by feature selection depending on expert experience can be avoided, the insulation defect type of the cable can still be accurately recognized under the condition of small samples, and the recognition rate and accuracy are improved.

Description

technical field [0001] The invention belongs to the technical field of cable partial discharge identification methods, and in particular relates to a cable partial discharge pattern identification method based on depth sample enhancement. Background technique [0002] High-voltage cables are one of the main forms of power transmission, and are widely used in wind farms, solar energy and other distributed energy transmission and grid connection, as well as offshore oil and gas platforms, offshore island power transmission, etc. , Cable body or accessory manufacturing quality, laying installation quality and other reasons cause cable operation failures, which will cause economic losses and social impacts to varying degrees. [0003] The traditional cable partial discharge pattern recognition method relies heavily on the selection of manual features, and the design of manual features is very dependent on expert experience, which will inevitably lead to errors. As an emerging fi...

Claims

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Application Information

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IPC IPC(8): G01R31/12
CPCG01R31/12
Inventor 杨贵营龙洁孙抗刘哲睿王海港郭琳李习斌
Owner JIAOZUO POWER SUPPLY COMPANY OF STATE GRID HENAN ELECTRIC POWER
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