Research on the Application of Artificial Neural Networks in Infrared Temperature Measurement of Power Equipment
Heating of power equipment in substations can pose significant risks to the operation of the power grid,greatly reducing power quality and supply reliability.Therefore,it is necessary to monitor the temperature of transformers,high-voltage switchgear,insulators,and wire contact points during routine inspections of substations to ensure the normal and stable operation of substation electrical equipment.The traditional method of temperature collection in substations is extremely time-consuming and labor-intensive,and due to the high voltage between phases and ground,the traditional temperature measurement method also poses a great threat to personnel safety.It is also prone to or missed detections,which can cause waste of personnel and resources.To address this phenomenon,conducting unmanned aerial vehicle inspection infrared photo automatic temperature measurement is an effective measure to ensure the stable operation of electrical equipment.How to quickly and automatically identify equipment temperature anomalies in unmanned aerial vehicle infrared inspection photos is an urgent problem to be solved.This article proposes a method of approximating the mapping relationship between RGB values of infrared image pixels and Celsius temperature values through an artificial neural network.The method takes the RGB values of infrared image pixels as the input of the artificial neural network,and the output of the network is the Celsius temperature value.We used infrared images obtained from routine inspections of substations as data sources to train a fully connected artificial neural network with three hidden layers.The test results showed that the artificial neural network had a good fitting degree for tower materials and insulators,with a deviation value of less than 1℃,and a poor fitting ability for trees and the sky with a deviation value of more than 1℃.
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