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Characteristic identification-based intelligent analysis method for power transmission line insulator flashovers

A transmission line and intelligent analysis technology, which is applied in the direction of using optical methods to test, measure electricity, and measure electrical variables, etc., can solve the problems of easy omission and misreporting of key information, easy omission of key information, etc., and achieve the goal of reducing the incidence of accidents Effect

Pending Publication Date: 2016-11-09
STATE GRID FUJIAN ELECTRIC POWER CO LTD +3
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AI Technical Summary

Problems solved by technology

However, the amount of images or video data acquired during the aerial inspection process is very large. If the method of manual screening, manual analysis and judgment is used to directly process the huge video and picture information, it is undoubtedly very hard work and very difficult. It is easy to miss key information; if manual online monitoring, real-time judgment and detection of abnormalities in power transmission and transformation lines are used, it will be more difficult work, and it is easier to miss and misreport key information

Method used

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Embodiment 1

[0014] An intelligent analysis method for flashover of transmission line insulators based on feature recognition, which is realized by the following steps:

[0015] (1) First, a large number of images are taken from the transmission line by drone, and the skeleton of the image is extracted, and the redundancy in the image is removed at the same time; then the image is detected based on the skeleton shape difference between the insulator and other power transmission equipment. This determines the candidate area of ​​the insulator, so as to realize the rough positioning of the insulator;

[0016] (2) Use the moment invariant feature and the AdaBoost algorithm to traverse and identify the candidate area of ​​the insulator, thereby identifying the precise position of the insulator, thereby realizing the precise positioning of the insulator;

[0017] (3) Firstly, the geometric features of the insulator are extracted through the edge detection algorithm, and the new object is descri...

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Abstract

The invention discloses a characteristic identification-based intelligent analysis method for power transmission line insulator flashovers. According to the characteristic identification-based intelligent analysis method for power transmission line insulator flashovers, machine vision analysis technologies having artificial intelligence significance is introduced, a conventional tour inspection system is enabled to have identification capability of human eyes and analyzing capability of human brains; via visual analysis, potential threatening events such as power transmission line insulator flashovers and the like can be identified and analyzed, a real time early warning function can be fulfilled, and accident rates can be lowered.

Description

technical field [0001] The invention relates to an intelligent analysis method for flashover of insulators in transmission lines, in particular to an intelligent analysis method for flashover of insulators in transmission lines based on feature identification. Background technique [0002] Transmission lines play a very important role in the power system and are directly related to the electricity consumption of thousands of households. Large-scale power outages will bring immeasurable losses to the national economy. Therefore, the safety of transmission lines is one of the issues of great concern to the power sector. As a large amount and various components in the power grid, insulators play an important role in transmission lines. On the one hand, they provide mechanical support for the wires that transmit current; on the other hand, they prevent the current from forming a channel to the ground and play an insulating role. However, insulators are extremely vulnerable comp...

Claims

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Application Information

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IPC IPC(8): G01R31/12
CPCG01R31/1218G01R31/1245
Inventor 詹仁俊林朝辉王力群许凌西吴飞龙王景
Owner STATE GRID FUJIAN ELECTRIC POWER CO LTD
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