Insulating tube bus fault diagnosis method based on LDA optimization multi-scale textural features

A texture feature and fault diagnosis technology, applied to fault locations, measuring devices, instruments, etc., can solve the problem that the recognition rate cannot reach a high level

Active Publication Date: 2020-09-04
SHANGHAI MUNICIPAL ELECTRIC POWER CO
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Problems solved by technology

These methods only extract single-dimensional texture features, and the recognition rate cannot reach a high level

Method used

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  • Insulating tube bus fault diagnosis method based on LDA optimization multi-scale textural features
  • Insulating tube bus fault diagnosis method based on LDA optimization multi-scale textural features
  • Insulating tube bus fault diagnosis method based on LDA optimization multi-scale textural features

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Embodiment Construction

[0068] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0069] A fault diagnosis method for insulated tube bases based on LDA to optimize multi-scale texture features. This method is a program embedded in the computer. The flow diagram is as follows figure 1 shown, including the following steps:

[0070] Step 1: Collect the partial discharge signal of the insulating tube parent, construct the PRPD spectrum, and then construct the PRPD spectrum into a Gaussian pyramid spectrum space;

[0071] Step 2: Extract th...

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Abstract

The invention relates to an insulating tube bus fault diagnosis method based on LDA optimization multi-scale texture features, and the method comprises the steps: 1, collecting an insulating tube buspartial discharge signal, constructing a PRPD spectrogram, and then constructing the PRPD spectrogram into a Gaussian pyramid spectrogram space; 2, extracting texture statistics of a Gaussian pyramidspectrogram space to form multi-scale texture features; 3, screening texture features through an LDA optimization method; 4, establishing a CART classification tree model according to the texture features screened in the step 3, and training and testing the model; and step 5, performing insulation tube bus fault diagnosis by using the CART classification tree model established in the step 4, and outputting a fault diagnosis result. Compared with the prior art, the method has the advantages of being high in recognition accuracy, capable of achieving automatic fault diagnosis, good in real-timeperformance and the like.

Description

technical field [0001] The invention relates to the technical field of partial discharge diagnosis, in particular to a method for diagnosing faults of insulating tube mothers based on LDA-based optimization of multi-scale texture features. Background technique [0002] Insulated tubular busbars (hereinafter referred to as insulating tubular busbars) are used in new compact substations due to their outstanding advantages such as low skin effect, large carrying capacity per unit section, good heat dissipation conditions, high mechanical strength, strong electrical insulation performance, and less maintenance workload. be widely used. As an important connecting conductor of the main transformer and the user's busbar, the insulating pipe mother directly affects the stability of the system operation and the reliability of the power supply. If the near-insulation pipe parent equipment fails, it will have a great impact on the safe and stable operation of the power grid and the re...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G01R31/08
CPCG01R31/088G06F18/2132G06F18/24323G06F18/214Y04S10/52
Inventor 周录波李荪琪刘君华周济平王大方王栋
Owner SHANGHAI MUNICIPAL ELECTRIC POWER CO
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