| Citation: |
YAN Danzhao, CHEN Jing, LAN Wangyao, LIAO Yipeng. Non-contact Diagnosis of Cable Joint Insulation Deterioration Based on Deep Learning Surface Temperature[J]. Infrared Technology , 2024, 46(6): 712-721.
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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.
| [1] |
曹培, 徐鹏, 高凯, 等. 基于边缘计算的电缆接头运行状态智能传感与监测[J]. 高压电器, 2020, 56(9): 26-32. https://www.cnki.com.cn/Article/CJFDTOTAL-GYDQ202009004.htm
CAO Pei, XU Peng, GAO Kai, et al. Intelligent sensing and monitoring of cable joints' state based on edge computing[J]. High Voltage Apparatus, 2020, 56(9): 26-32. https://www.cnki.com.cn/Article/CJFDTOTAL-GYDQ202009004.htm
|
| [2] |
揭青松, 杨庆, 崔浩楠, 等. 基于暂态电压传递特性的电缆接头绝缘状态检测方法[J]. 高电压技术, 2022, 48(3): 1124-1132. https://www.cnki.com.cn/Article/CJFDTOTAL-GDYJ202203033.htm
JIE Qingsong, YANG Qing, CUI Haonan, et al. Insulation state detection method of cable joint based on transient voltage transfer characteristics[J]. High Voltage Engineering, 2022, 48(3): 1124-1132. https://www.cnki.com.cn/Article/CJFDTOTAL-GDYJ202203033.htm
|
| [3] |
聂永杰, 赵现平, 李盛涛. XLPE电缆状态监测与绝缘诊断研究进展[J]. 高电压技术, 2020, 46(4): 1361-1371. https://www.cnki.com.cn/Article/CJFDTOTAL-GDYJ202004029.htm
NIE Yongjie, ZHAO Xianping, LI Shengtao. Research progress in condition monitoring and insulation diagnosis of XLPE cable[J]. High Voltage Engineering, 2020, 46(4): 1361-1371. https://www.cnki.com.cn/Article/CJFDTOTAL-GDYJ202004029.htm
|
| [4] |
陈浩, 徐阳, 钱森, 等. 分布式光纤超声传感器用于检测电缆接头放电故障[J]. 光学学报, 2020, 41(3): 0306001. https://www.cnki.com.cn/Article/CJFDTOTAL-GXXB202103003.htm
CHEN Hao, XU Yang, QIAN Sen, et al. Distributed fiber-optic ultrasonic sensor applied in detection on discharging fault of power cable joint[J]. Acta Optica Sinica, 2020, 41(3): 0306001. https://www.cnki.com.cn/Article/CJFDTOTAL-GXXB202103003.htm
|
| [5] |
LI L L, YONG J, XU W. On-line cable condition monitoring using natural power disturbances[J]. IEEE Transactions on Power Delivery, 2019, 34(4): 1242-1250. DOI: 10.1109/TPWRD.2018.2879964
|
| [6] |
高云鹏, 谭甜源, 刘开培, 等. 电缆接头温度反演及故障诊断研究[J]. 高电压技术, 2016, 42(2): 535-542. https://www.cnki.com.cn/Article/CJFDTOTAL-GDYJ201602027.htm
GAO Yunpeng, TAN Tianyuan, LIU Kaipei, et al. Research on temperature retrieval and fault diagnosis of cable joint[J]. High Voltage Engineering, 2016, 42(2): 535-542. https://www.cnki.com.cn/Article/CJFDTOTAL-GDYJ201602027.htm
|
| [7] |
古亮, 赵阿琴. 基于ZigBee的电缆接头温度在线监测系统设计[J]. 传感器与微系统, 2019, 38(6): 115-121. https://www.cnki.com.cn/Article/CJFDTOTAL-CGQJ201906033.htm
GU Liang, ZHAO Aqin. Design of on-line temperature monitoring system for cable connector based on ZigBee technology[J]. Transducer and Microsystem Technologies, 2019, 38(6): 115-121. https://www.cnki.com.cn/Article/CJFDTOTAL-CGQJ201906033.htm
|
| [8] |
邓志飞, 鲍光海. 基于超高频RFID技术的电缆接头温度在线监测系统[J]. 仪表技术与传感器, 2021(7): 71-75. https://www.cnki.com.cn/Article/CJFDTOTAL-YBJS202107014.htm
DENG Zhifei, BAO Guanghai. Online monitoring system of cable joint temperature based on UHF RFID technology[J]. Instrument Technique and Sensor, 2021(7): 71-75. https://www.cnki.com.cn/Article/CJFDTOTAL-YBJS202107014.htm
|
| [9] |
何宁辉, 周秀, 马波, 等. 基于神经网络和温度特性曲线的电缆故障率估计[J]. 电力科学与技术学报, 2022, 37(4): 169-174. https://www.cnki.com.cn/Article/CJFDTOTAL-CSDL202204019.htm
HE Ninghui, ZHOU Xiu, MA Bo, et al. Cable failure rate estimation based on neural network and temperature characteristic curve[J]. Journal of Eiectric Power Science and Technology, 2022, 37(4): 169-174. https://www.cnki.com.cn/Article/CJFDTOTAL-CSDL202204019.htm
|
| [10] |
HE B L, HUANG Y, YE T, et al. Temperature prediction of power cable joint based on PSO-LSSVM predict model[J]. Electric Power Engineering Technology, 2019, 38(1): 31-35.
|
| [11] |
曾庆煜. 基于机器学习的配网电力电缆接头温度预测及预警研究[D]. 南昌: 南昌大学, 2021.
ZENG Qingyu. Research on Prediction and early warning of Power cable joint temperature in Distribution Network based on Machine learning [D]. Nanchang: Nanchang University, 2021.
|
| [12] |
李胜辉, 白雪, 董鹤楠, 等. 基于平稳小波变换与随机森林的电缆早期故障识别方法[J]. 电工电能新技术, 2020, 39(3): 40-48. https://www.cnki.com.cn/Article/CJFDTOTAL-DGDN202003006.htm
LI Shenghui, BAI Xue, DONG Henan, et al. Cable incipient fault identification based on stationary wavelet transform and random forest[J]. Advanced Technology of Electrical Engineering and Energy, 2020, 39(3): 40-48. https://www.cnki.com.cn/Article/CJFDTOTAL-DGDN202003006.htm
|
| [13] |
徐四勤, 黄向前, 杨昆, 等. 基于温度以及运行数据的电缆接头绝缘劣化状态预测[J]. 计算机科学, 2022, 49(10): 132-137. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJA202210017.htm
XU Siqin, HUANG Xiangqian, YANG Kun, et al. Prediction of insulation deterioration state of cable joint based on temperature and running data[J]. Computer Science, 2022, 49(10): 132-137. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJA202210017.htm
|
| [14] |
CHENG Y S, ZHAO D W, WANG Y B, et al. Multi-label learning with kernel extreme learning machine autoencoder[J]. Knowledge-Based Systems, 2019, 178: 1-10.
|
| [15] |
廖一鹏, 杨洁洁, 王志刚, 等. 基于双模态卷积神经网络自适应迁移学习的浮选工况识别[J]. 光子学报, 2020, 49(10): 1015001. https://www.cnki.com.cn/Article/CJFDTOTAL-GZXB202010020.htm
LIAO Yipeng, YANG Jiejie, WANG Zhigang, et al. Flotation performance recognition based on dual-modality convolutional neural network adaptive transfer learning[J]. Acta Photonica Sinica, 2020, 49(10): 1015001. https://www.cnki.com.cn/Article/CJFDTOTAL-GZXB202010020.htm
|
| [16] |
段礼祥, 刘子旺, 赵振兴, 等. 基于区域对比和随机森林的设备故障红外图像敏感区域提取[J]. 红外技术, 2020, 42(10): 988-993. http://hwjs.nvir.cn/cn/article/id/hwjs202010012
DUAN Lixiang, LIU Ziwang, ZHAO Zhenxin, et al. Infrared image roi extraction based on region contrast and random forest[J]. Infrared Technology, 2020, 42(10): 988-993. http://hwjs.nvir.cn/cn/article/id/hwjs202010012
|
| [17] |
TKACHUK V. Quantum genetic algorithm based on qutrits and its application[J]. Mathematical Problems in Engineering, 2018(4): 8614073.
|
| [18] |
GAO Y J, ZHANG F M, ZHAO Y, et al. A novel quantum-inspired binary wolf pack algorithm for difficult knapsack problem[J]. International Journal of Wireless and Mobile Computing, 2019, 16(3): 222-232.
|
| [19] |
YU S J, ZHU J P, CHUN F. A quantum annealing bat algorithm for node localization in wireless sensor networks[J]. Sensors, 2023, 23(2): 782-793.
|
| [20] |
王坤, 刘沛伦, 王力. 基于FFWA的自适应Canny飞机蒙皮红外图像边缘检测[J]. 红外技术, 2021, 43(5): 443-454. http://hwjs.nvir.cn/cn/article/id/552e4afd-1c02-4e20-9023-b11bc6b0c05b
WANG Kun, LIU Peilun, WANG Li. Infrared image adaptive Canny edge-detection of aircraft skin based on fast fireworks algorithm[J]. Infrared Technology, 2021, 43(5): 443-454. http://hwjs.nvir.cn/cn/article/id/552e4afd-1c02-4e20-9023-b11bc6b0c05b
|
| [21] |
林剑萍, 廖一鹏. 结合分数阶显著性检测及量子烟花算法的NSST域图像融合[J]. 光学精密工程, 2021, 29(6): 1406-1419. https://www.cnki.com.cn/Article/CJFDTOTAL-GXJM202106021.htm
LIN Jianping, LIAO Yipeng. A novel image fusion method with fractional saliency detection and QFWA in NSST[J]. Optics and Precision Engineering, 2021, 29(6): 1406-1419. https://www.cnki.com.cn/Article/CJFDTOTAL-GXJM202106021.htm
|
| [22] |
XU Z N, HU Z W, Zhao L J, et al. Application of temperature field modeling in monitoring of optic-electric composite submarine cable with insulation degradation[J]. Measurement, 2019, 133: 479-494.
|
| [23] |
王耀祥, 田维坚, 章兴龙, 等. 纤维光锥有效透过率的理论分析[J]. 光子学报, 2005, 34(4): 529-533. https://www.cnki.com.cn/Article/CJFDTOTAL-GZXB20050400C.htm
WANG Yaoxiang, TIAN Weijian, ZHANG Xinglong, et al. Theoretical analysis of the effective transmission about fiber taper[J]. Acta Photonica Sinica, 2005, 34(4): 529-533. https://www.cnki.com.cn/Article/CJFDTOTAL-GZXB20050400C.htm
|
| [24] |
王耀祥, 田维坚, 黄琨, 等. 光锥与CCD耦合效率的理论分析[J]. 光子学报, 2004, 33(3): 318. https://www.cnki.com.cn/Article/CJFDTOTAL-GZXB200403016.htm
WANG Yaoxiang, TIAN Weijian, HUANG Kun, et al. Theoretical analysis of the coupling efficient between fiber taper and CCD[J]. Acta Photonica Sinica, 2004, 33(3): 318. https://www.cnki.com.cn/Article/CJFDTOTAL-GZXB200403016.htm
|
| [25] |
李晓峰, 李莉, 邓华斌, 等. 光纤面板及光锥传像特性研究(英文)[J]. 红外技术, 2014, 36(8): 617-623. http://hwjs.nvir.cn/cn/article/id/hwjs201408003
LI Xiaofeng, LI Li, DENG Huabin, et al. Study on light transmission characteristics of fiber optic faceplate and fiber optic taper[J]. Infrared Technology, 2014, 36(8): 617-623. http://hwjs.nvir.cn/cn/article/id/hwjs201408003
|
| [26] |
辛福学. ICCD的光纤耦合技术[J]. 红外与激光工程, 2001(3): 210-213. https://www.cnki.com.cn/Article/CJFDTOTAL-HWYJ201507033.htm
XIN Fuxue. Optical fiber coupling technique of ICCD[J]. Infrared and Laser Engineering, 2001(3): 210-213. https://www.cnki.com.cn/Article/CJFDTOTAL-HWYJ201507033.htm
|
| [27] |
Smith A W. Textured fiber optic coupled image intensified camera: U. S. Patent 9, 201, 193[P]. 2015-12-1.
|
| [28] |
DU Y, HUANG Y, JIAO P, et al. Coupling resolution of tapered optical fiber array and CCD[C]//Ninth Symposium on Novel Photoelectronic Detection Technology and Applications of SPIE, 2023, 12617: 2002-2008.
|
| [29] |
何欢. 距离选通ICCD及其控制电路设计与实现[D]. 中国科学院研究生院(西安光学精密机械研究所), 2015.
HE Huan, Design for a Range-Gated ICCD and Its Control Circuit[D]. Xi'an: Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, 2015.
|
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