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A neural network based prediction method for bird damage state of transmission lines

A neural network and transmission line technology, which is applied in the field of bird damage status estimation of transmission lines based on neural network, can solve problems such as huge battery power consumption, and achieve the effects of reducing the number of starts, strong practical value, and reduced power consumption.

Inactive Publication Date: 2019-01-29
GUANGDONG UNIV OF TECH
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AI Technical Summary

Problems solved by technology

The bird repellers on the line are generally powered by solar cells, so energy-saving measures must be taken. However, once the existing bird repellers detect birds, they will make a chirping sound, which consumes a lot of battery power.

Method used

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  • A neural network based prediction method for bird damage state of transmission lines
  • A neural network based prediction method for bird damage state of transmission lines
  • A neural network based prediction method for bird damage state of transmission lines

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Embodiment Construction

[0031] The present invention will be further described in detail below in conjunction with the embodiments and the accompanying drawings, but the embodiments of the present invention are not limited thereto.

[0032] Such as figure 1 As shown, a neural network-based bird damage state estimation method for transmission lines includes the following steps:

[0033] Step 1, using the Doppler radar to detect the flying bird activities near the transmission line tower, obtaining the flight track information of the flying bird, and extracting the characteristic information of the flight track;

[0034] Step 2, constructing the mathematical model of flight trajectory neural network prediction:

[0035] x(k)=h(k-1)+αx(k-1),

[0036]

[0037] y(k)=g(w 4 (k)h(k)),

[0038] Among them, x(k) represents the output of the receiving layer of the k-th iteration, h(k) represents the hidden output of the k-th iteration, O(k) represents the output of the output layer of the k-th iteration,...

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Abstract

The invention discloses a neural network based prediction method for bird damage state of transmission lines, which comprises the following steps: S1, detecting bird activity near transmission line pole and tower by Doppler radar, obtaining bird flight trajectory information and extracting flight trajectory characteristic information; S2, constructing a flight trajectory prediction mathematical model based on neural network; S3, train a mathematical model network of a flight path prediction neural network by adopting a Bayesian regularization algorithm; the invention can effectively reduce thestartup times of the bird prevention device in the aspect of bird damage prevention of the transmission line, thereby greatly reducing the electric power consumption of the existing bird prevention device, realizing that the existing bird prevention device achieves intelligent bird driving, providing great convenience for the subsequent patrol and inspection personnel, and having strong practicalvalue.

Description

technical field [0001] The invention relates to the technical field of power transmission line patrol inspection, in particular to a method for estimating the bird damage state of a power transmission line based on a neural network. Background technique [0002] The safe operation of transmission lines is crucial to ensure uninterrupted power supply to users. The current bird damage accident has become a major hidden danger affecting the safe operation of transmission lines, and has attracted more and more attention from the power sector. At present, the domestic anti-bird measures mainly include installing windmills and terror eyes for frightening birds on the line towers, hanging small red flags, and installing anti-bird thorns. Studies have found that the best effect of repelling birds is to use the unique, genetically common, and biologically meaningful calls of bird species such as alarming or dying calls, and calling for help. Bird repellents on the line are generall...

Claims

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Application Information

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IPC IPC(8): G06Q10/06G06Q50/06G06N3/04
CPCG06Q10/0635G06Q50/06G06N3/045
Inventor 张斌林文帅鲁仁全周琪李鸿一
Owner GUANGDONG UNIV OF TECH
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