Electric power external insulation equipment typical defect fault identification method based on ultraviolet imaging

Abstract

The invention discloses an electric power external insulation equipment typical defect fault identification method based on ultraviolet imaging, and belongs to the technical field of electric power grids. The invention aims to solve the problem of low efficiency of identifying corona discharge defect faults through an ultraviolet spectrum in the prior art. The method comprises the following steps: simulating external insulation defects of power equipment, and acquiring an ultraviolet spectrum of corona discharge of the power equipment with the simulated defects by adopting an ultraviolet imaging technology; establishing a neural network, training the neural network by using the ultraviolet spectrum and the defect fault corresponding to the discharge, and obtaining a fault recognition model based on the ultraviolet discharge spectrum; and collecting the ultraviolet discharge spectrum of the to-be-detected electric power external insulation equipment, inputting the collected ultraviolet discharge spectrum into the fault identification model based on the ultraviolet discharge spectrum, and obtaining the fault defect of the to-be-detected electric power external insulation equipment. The method is suitable for typical defect fault identification of the power external insulation equipment.

Description

Technical Field

[0001] The invention belongs to the technical field of electric power grid. Background Art

[0002] UV imaging generally uses photon multiplication imaging technology, and its imaging principle is to use the electronic optical system to convert ultraviolet light into a fluorescent screen image. Ultraviolet imaging has long been used in the field of electrical equipment status detection technology. The solar-blind ultraviolet detection technology based on the principle of half-mirror cleverly solves the influence of sunlight on ultraviolet imaging, making ultraviolet light appear on the image of visible light, and realizing discharge counting, discharge intensity evaluation and other functions. However, how to quickly identify corona discharge defects through ultraviolet spectra is still an urgent problem to be solved. Summary of the invention

[0003] The present invention aims to solve the problem of low efficiency in identifying corona discharge defect faults through ultraviolet spectra, and provides a method for identifying typical defect faults of power external insulation equipment based on ultraviolet imaging.

[0004] The method for identifying typical defects and faults of power external insulation equipment based on ultraviolet imaging of the present invention comprises:

[0005] Step 1: simulate the external insulation defects of the power equipment, and use ultraviolet imaging technology to obtain the ultraviolet spectrum of the corona discharge of the power equipment with simulated defects;

[0006] Step 2: Establish a neural network, use the ultraviolet spectrum and the corresponding discharge defect faults to train the neural network, and obtain a fault recognition model based on the ultraviolet discharge spectrum;

[0007] Step three: collect the ultraviolet discharge spectrum of the power external insulation equipment to be detected, input the collected ultraviolet discharge spectrum into a fault identification model based on the ultraviolet discharge spectrum, and obtain the fault defects of the power external insulation equipment to be detected.

[0008] Furthermore, in the present invention, the typical defects of external insulation in step one include: simulated composite insulators, porcelain insulators, post insulators, transmission lines, transformer external insulation, and contamination of external insulation equipment.

[0009] The present invention adopts ultraviolet spectrum technology to obtain the corona discharge phenomenon of power equipment, obtains the ultraviolet discharge spectrum corresponding to the discharge phenomenon, and uses the ultraviolet spectrum of corona discharge and the corresponding faults to train a neural network, which effectively improves the recognition speed of typical defects in the external insulation of power equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0011] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

[0012] Specific implementation method 1: refer to Figure 1 Specifically describing this embodiment, the method for identifying typical defects and faults of power external insulation equipment based on ultraviolet imaging described in this embodiment includes:

[0013] Step 1: simulate the external insulation defects of the power equipment, and use ultraviolet imaging technology to obtain the ultraviolet spectrum of the corona discharge of the power equipment with simulated defects;

[0014] Step 2: Establish a neural network, use the ultraviolet spectrum and the corresponding discharge defect faults to train the neural network, and obtain a fault recognition model based on the ultraviolet discharge spectrum;

[0015] Step three: collect the ultraviolet discharge spectrum of the power external insulation equipment to be detected, input the collected ultraviolet discharge spectrum into a fault identification model based on the ultraviolet discharge spectrum, and obtain the fault defects of the power external insulation equipment to be detected.

[0016] Furthermore, in the present invention, the typical defects of external insulation in step one include: simulated composite insulators, porcelain insulators, post insulators, transmission lines, transformer external insulation, and contamination of external insulation equipment.

[0017] The present invention simulates typical faults in a simulation manner, effectively ensuring a more comprehensive fault type, and obtains a fault diagnosis model after training with a neural network. When identifying typical defects in the external insulation of power equipment, it is only necessary to collect the ultraviolet spectrum of the equipment and input it into the neural network to obtain the corresponding fault type.

[0018] Although the present invention is described herein with reference to specific embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the present invention. It should therefore be understood that many modifications may be made to the exemplary embodiments and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the appended claims. It should be understood that the various dependent claims and features described herein may be combined in a manner different from that described in the original claims. It should also be understood that the features described in conjunction with a single embodiment may be used in other described embodiments.

Claims

1. A typical defect fault identification method for power external insulation equipment based on ultraviolet imaging, characterized in that: The method includes Step 1: simulate the external insulation defects of the power equipment, and use ultraviolet imaging technology to obtain the ultraviolet spectrum of the corona discharge of the power equipment with simulated defects; Step 2: Establish a neural network, use the ultraviolet spectrum and the corresponding discharge defect faults to train the neural network, and obtain a fault recognition model based on the ultraviolet discharge spectrum; Step three: collect the ultraviolet discharge spectrum of the power external insulation equipment to be detected, input the collected ultraviolet discharge spectrum into a fault identification model based on the ultraviolet discharge spectrum, and obtain the fault defects of the power external insulation equipment to be detected.

2. The method for identifying typical defects and faults of power external insulation equipment based on ultraviolet imaging according to claim 1 is characterized in that: The typical defects of external insulation described in step 1 include: simulated composite insulators, porcelain insulators, post insulators, transmission lines, transformer external insulation, and contamination of external insulation equipment.

Images