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Power equipment classification method based on deep learning under small sample

A technology of power equipment and deep learning, applied in the direction of neural learning methods, instruments, biological neural network models, etc., to achieve the effect of reducing dependence, reducing manual labor, and good classification effect

Pending Publication Date: 2019-09-24
YANGZHOU UNIV
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Problems solved by technology

But deep learning requires a large number of labeled samples, and it takes a lot of time to train

Method used

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  • Power equipment classification method based on deep learning under small sample
  • Power equipment classification method based on deep learning under small sample
  • Power equipment classification method based on deep learning under small sample

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Embodiment Construction

[0012] Aiming at the problems of low classification accuracy of infrared images of current power equipment and high degree of manual dependence, the present invention proposes a classification method of power equipment based on deep learning under small samples; the infrared image of power equipment is used as input, and the power equipment is classified by deep learning method. Equipment classification, the implementation scheme will be described in detail below.

[0013] Such as figure 1 As shown, a classification method of power equipment based on deep learning under small samples, including the following steps:

[0014] Step 1: Take infrared images on the inspection track with an infrared thermal imager. The electrical equipment included in the collected infrared images includes: bushings, lightning arresters, wall bushings, wires, cable terminals, power cables, and power capacitors , Current transformers, voltage transformers, terminal boxes, circuit breakers, discharge ...

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Abstract

The invention discloses a power equipment classification method based on deep learning under a small sample in the field of image processing. The power equipment classification method comprises the following steps: step 1, obtaining a standardized power equipment infrared image through a substation equipment detection device; step 2, establishing an electric power equipment infrared image sample library, and making a training set, a verification set and a test set; step 3, establishing a small sample learning network, training the established convolutional neural network by using a training set of a sample library, verifying the model through a verification set, and obtaining a connection weight and a bias parameter of the network model after training; and step 4, classifying the infrared images in the test set by using the trained network model to generate a classification result of the infrared images of the power equipment, obtaining a good effect under the condition that the sample size is relatively small, and not needing a large amount of training time, and being applicable to management and control of the power equipment.

Description

technical field [0001] The invention relates to electric equipment, in particular to a method for classifying electric equipment. Background technique [0002] The infrared image of the power equipment is to detect the infrared radiation energy emitted by the power equipment and convert it into a corresponding electrical signal. After the electrical signal is processed, the thermal image of the surface of the power equipment is obtained. Infrared detection technology has the characteristics of long-distance, no contact, no sampling, no disassembly, accuracy, speed, and intuitiveness. It is widely used in the detection and diagnosis of power equipment, and is of great significance to improving the stability of power systems. But the infrared image is a kind of false color image, which reflects the level and distribution of the surface temperature of the object, and has the characteristics of concentrated intensity and low contrast. And limited by the technology of infrared i...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08G06Q50/06
CPCG06N3/08G06Q50/06G06N3/045G06F18/241
Inventor 郭志波崔正大姚新
Owner YANGZHOU UNIV
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