红外技术, 2023, 45 (12): 1351, 网络出版: 2024-01-17  

基于红外成像的中低压电网电力稳定器高温运行 可靠性图像识别方法

Reliability Image Recognition Method for High Temperature Operation of Power Stabilizer in Medium and Low Voltage Grids Based on Infrared Imaging
作者单位
1 国网辽宁省电力有限公司, 辽宁沈阳, 110000
2 国网辽宁省电力有限公司电力科学研究院, 辽宁沈阳, 110000
3 国网辽宁省电力有限公司营口供电公司, 辽宁营口 115000
4 国网辽宁省电力有限公司锦州供电公司, 辽宁锦州 121000
5 国网辽宁省电力有限公司铁岭供电公司, 辽宁铁岭 112000
摘要
电力稳定器在电网中起到稳定电压的作用, 一旦该设备出现异常, 电网运输电力质量会受到直接影响。面对这种情况, 研究一种基于红外成像技术的中低压电网电力稳定器高温运行可靠性图像识别技术。该研究中利用红外成像技术采集电力稳定器图像并实施预处理。分割电力稳定器红外图像, 划分目标区域和背景区域。提取目标区域 5个直方图-阶统计特征。以 5个直方图-阶统计特征为基础, 结合判别系数, 构建分类器, 实现电力稳定器状态识别。针对存在异常的电力稳定器, 计算图像目标区域处的相对温差, 确定可靠性等级。结果表明: 5个测试稳定器中只有 2个稳定器处在异常状态, 具体为稳定器 2中组成部分 3异常, 稳定器 5中组成部分 1异常。稳定器 2组成部分 3相对温差为 82.32%, 对应可靠等级为 2级, 可靠性低; 稳定器 5组成部分 1相对温差为 91.35%, 对应可靠等级为 3级, 可靠性非常低。对比实验结果表明, 所提方法识别准确率达到 92.3%以上, 优于对比方法, 具有更大的应用价值。
Abstract
area was calculated to determine the reliability level. The results show that only two of the five test stabilizers are in an abnormal state; specifically, component 3 of stabilizer 2 is abnormal, and component 1 of stabilizer 5 is abnormal. The relative temperature difference of component 3 of stabilizer 2 was 82.32%, and the corresponding reliability level was level 2, with low reliability; the relative temperature difference of component 1 of stabilizer 5 was 91.35%, the corresponding reliability level was level 3, and the reliability was extremely low. Comparative experimental results show that the recognition accuracy of the proposed method reaches 92.3% or higher, which is superior to that of the comparison method and has a greater application value.
参考文献

[1] Gavoshani A, Orouji A A. A novel deep gate power MOSFET in partial SOI technology for achieving high breakdown voltage and low lattice temperature[J]. Journal of Computational Electronics, 2021, 20(9): 1-7.

[2] OUYANG Q, WANG L, Park B, et al. Simultaneous quantification of chemical constituents in matcha with visible-near infrared hyperspectral imaging technology[J]. Food Chemistry, 2021, 350(6): 129141.

[3] DU Houxian, LIU Hao, LEI Longwu, et al. Power transformer fault detection based on multi-eigenvalues of vibration signal[J]. Transactions of China Electrotechnical Society, 2023, 38(1): 83-94.

[4] ZHANG Zhaoyu, HU Yidan, SONG Yanfeng, et al. Development and application of mechanical vibration ultrasonic fusion detection sensor for electric power equipment[J]. Chinese Journal of Electrical Engineering, 2023, 43(14): 5713-5723.

[5] WANG Kaixuan, REN Fuji, NI Hongjun, et al. Temperature value recognition algorithm for the infrared image of power equipment[J]. CAAI Transactions on Intelligent Systems, 2022, 17(3): 617-624.

[6] HUANG H, HU X, TIAN J, et al. Rapid and nondestructive prediction of amylose and amylopectin contents in sorghum based on hyperspectral imaging[J]. Food Chemistry, 2021, 359(8): 129954.

[7] Daradkeh Y I, Tvoroshenko I, Gorokhovatskyi V, et al. Development of effective methods for structural image recognition using the principles of data granulation and apparatus of fuzzy logic[J]. IEEE Access, 2021, 9(99): 13417-13428.

[8] WANG Y, LIU H, GUO M, et al. Image recognition model based on deep learning for remaining oil recognition from visualization experiment[J]. Fuel, 2021, 291(3): 120216.

[9] CHEN M, WANG X, LUO H, et al. Learning to focus: cascaded feature matching network for few-shot image recognition[J]. Science China Information Sciences, 2021, 64(9): 192105.

[10] GAO P, ZHAO D, CHEN X. Multi-dimensional data modelling of video image action recognition and motion capture in deep learning framework[J]. IET Image Processing, 2020, 14(7): 1257-1264.

[11] Karunakaran V, Saritha V N, Ramya A N, et al. Elucidating Raman image-guided differential recognition of clinically confirmed grades of cervical exfoliated cells by dual biomarker-appended SERS-tag[J]. Analytical Chemistry, 2021, 93(32): 11140-11150.

[12] ZHAO Y, WANG C, PEI J, et al. Nonlinear loose coupled non-negative matrix factorization for low-resolution image recognition[J]. Neurocomputing, 2021, 443(8): 183-198

[13] Andriyanov N A, Dementiev V E, Kargashin Y D. Analysis of the impact of visual attacks on the characteristics of neural networks in image recognition[J]. Procedia Computer Science, 2021, 186(12): 495-502.

[14] WANG F, HU R, JIN Y. Research on gesture image recognition method based on transfer learning[J]. Procedia Computer Science, 2021, 187(10): 140-145.

[15] Quionez Y, Lizarraga C, Peraza J, et al. Image recognition in UAV videos using convolutional neural networks[J]. IET Software, 2020, 14(2): 176-181.

[16] Corti E, Khanna A, Niang K, et al. Time-delay encoded image recognition in a network of resistively coupled VO2 on Si oscillators[J]. IEEE Electron Device Letters, 2020, 41(4): 629-632.

代子阔, 史可鉴, 宋仕达, 刘扬, 徐妍. 基于红外成像的中低压电网电力稳定器高温运行 可靠性图像识别方法[J]. 红外技术, 2023, 45(12): 1351. DAI Zikuo, SHI Kejian, SONG Shida, LIU Yang, XU Yan. Reliability Image Recognition Method for High Temperature Operation of Power Stabilizer in Medium and Low Voltage Grids Based on Infrared Imaging[J]. Infrared Technology, 2023, 45(12): 1351.

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