Abstract
Various defects of power equipment affect the normal operation of power grid, and serious defects even bring great losses to production and life. Infrared image recognition of power equipment is a necessary prerequisite to realize condition monitoring and fault diagnosis of power equipment under infrared imaging. Because of the imaging characteristics of infrared images, the complexity of background environment and the diversity and difference of power equipment itself, it is difficult to identify infrared images of power equipment. The purpose of this article is to propose a fast and accurate condition monitoring method for power equipment. Based on this, this study proposes an infrared image recognition algorithm of power equipment based on improved Convolutional Neural Network (CNN), which provides technical support for the construction of power equipment condition monitoring system. The simulation results show that the objective function of the improved model can achieve stable payment with fewer iterations, and it is superior to the traditional Support Vector Machine (SVM) algorithm and Ant Colony Optimization (ACO) algorithm in terms of accuracy, recall and running time, thus verifying the effectiveness of the algorithm and the interference to different backgrounds.
Published Version
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