基于改进SSD算法和紫外成像的悬式绝缘子放电严重程度评估

Discharge Severity Assessment of Suspension Insulator Based on Improved SSD Algorithm and UV Imaging

为实现基于边缘检测的悬式绝缘子放电日盲紫外成像智能评估,论文搭建了悬式绝缘子污秽放电试验平台,获得了相对光斑面积等紫外参数并提出了放电严重程度判据,建立了不同放电程度的紫外图像样本数据库;采用改进的SSD模型对紫外放电视频流进行训练,实现了模型参数缩减以及训练收敛速度的提升。研究结果表明:采用改进后的SSD模型对放电图像进行训练,模型文件大小为4.5MB,具有良好的嵌入式检测性能;在模型训练过程中,设计了自主更新学习率机制,使得模型的收敛速度提升约3.5倍,收敛值降低1/2。可将绝缘子的放电严重程度智能评估为“slight”、“moderate”、“heavy”、“worse”四个等级,论文的研究成果为边缘侧基于紫外成像的电力设备放电严重程度的实时评估提供了新思路。

In order to realize the intelligent evaluation of day-blind UV imaging of suspended insulator discharge based on edge detection,this paper established a test platform of suspended insulator pollution discharge and obtained the UV parameters such as relative spot area.Based on this,we put forward the criterion of discharge severity,and established the UV image sample database of different discharge degrees.The improved SSD model was used to train the UV TV frequency stream,which realized the reduction of model parameters and the improvement of training convergence speed.The results show that,when the improved SSD model is used to train the discharge image,the model file size is 4.5mb and has good embedded detection performance.In the process of model training,we designed the self-updating learning rate mechanism to increase the convergence speed of the model by about 3.5 times and reduce the convergence value by 1/2.The intelligent assessment of insulator discharge severity can be divided into four grades:“slow”,“moderate”,“heavy”and“worse”.The results of this paper provide a new idea for the real-time assessment of power equipment discharge severity based on UV imaging on the edge side.