基于表层温度深度学习的电缆接头绝缘劣化非接触式诊断  

Non-contact Diagnosis of Cable Joint Insulation Deterioration Based on Deep Learning Surface Temperature

作　　者：严丹昭 陈晶 兰旺耀 廖一鹏[4] YAN Danzhao;CHEN Jing;LAN Wangyao;LIAO Yipeng(Fuzhou Yili Electric Power Engineering Co.,Ltd.Distribution Network Construction Branch,Fuzhou 350000,China;State Grid Fujian Electric Power Co.,Ltd.,Fuzhou Power Supply Company,Fuzhou 350009,China;Fuzhou Zhongxiang Electronic Information Technology Co.Ltd.,Fuzhou 350026,China;College of Physics and Information Engineering,Fuzhou University,Fuzhou 350108,China)

机构地区：[1]福州亿力电力工程有限公司配网建设分公司,福建福州350000 [2]国网福建省电力有限公司福州供电公司,福建福州350009 [3]福建众想电子信息科技有限公司,福建福州350026 [4]福州大学物理与信息工程学院,福建福州350108

出　　处：《红外技术》2024年第6期712-721,共10页Infrared Technology

基　　金：国家自然科学基金(62271149,62271151);福州亿力电力工程有限公司配电工程分公司资助项目(RNFW2022GJT041013-Z)。

摘　　要：为提升电缆中间接头绝缘层劣化程度的现场诊断效率和准确度,提出一种基于表层温度自适应深度学习的接头绝缘劣化状态非接触式诊断方法。首先,对电缆接头及两端电缆的绝缘表层进行红外热成像,实现电缆接头中心两边多个对称区域的表层温度、接头两端电缆的表层温度的非接触式采集;其次,构建基于双隐层自编码极限学习机的深度学习网络,以挖掘表层温度数据内部深层次隐含特征,将提取的深度隐含特征作为随机森林诊断模型输入;然后,提出一种非线性动态自适应旋转角的量子旋转门以改进量子烟花算法的更新策略,并用于诊断模型参数优化;最后,结合接头表层红外温度和绝缘介质损耗角正切值构建数据集,对诊断模型进行训练和现场测试。实验结果表明,改进后的量子烟花算法可以较好地逼近全局最优解、收敛效率高,深度学习随机森林诊断模型具有较强的特征抽取和分类能力,参数优化后诊断模型的分类精度和稳定性得到有效提高,在小样本训练集条件下就能达到较好的诊断效果,可实现接头绝缘劣化状态的非接触式诊断。To improve the efficiency and accuracy of the field diagnosis of insulation layer deterioration of the cable intermediate joint,a non-contact diagnosis method based on adaptive deep learning of surface temperature is proposed.First,infrared thermal imaging was performed on the insulating surface of the cable joint and cables at both ends.The surface temperatures of multiple symmetric areas on both sides of the center of the cable joint and cables at both ends were collected without contact.Subsequently,a deep learning network based on a two-hidden autoencoder extreme learning machine was constructed to mine the deep hidden features in the surface temperature data.The extracted deep hidden features were used as input to the random forest diagnosis model.A quantum rotation gate with a nonlinear dynamic adaptive rotation angle was further proposed to improve the update strategy of the quantum firework algorithm and optimize the parameters of the diagnostic model.Finally,by combining the infrared temperature of the joint surface and loss angle tangent value of the insulating medium,a dataset was constructed to train and test the diagnostic model in the field.The experimental results show that the improved quantum fireworks algorithm can better approximate the global optimal solution and has high convergence efficiency.The deep learning random forest diagnostic model exhibited strong feature extraction and classification capabilities,whereby the classification accuracy and stability of the diagnostic model were effectively improved after parameter optimization,and better diagnostic results were achieved under the condition of a small sample training set.Therefore,noncontact diagnosis of joint insulation deterioration is achievable.

关 键 词：电缆中间接头 红外测温 绝缘劣化诊断 双隐层自编码极限学习机 随机森林 量子烟花算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]