ZHANG Meijin, QU Qiubo. Infrared Thermography Low-zero Insulator Identification Based on GWO-SVM[J]. Infrared Technology , 2021, 43(4): 397-402.
Citation: ZHANG Meijin, QU Qiubo. Infrared Thermography Low-zero Insulator Identification Based on GWO-SVM[J]. Infrared Technology , 2021, 43(4): 397-402.

Infrared Thermography Low-zero Insulator Identification Based on GWO-SVM

More Information
  • Received Date: August 07, 2019
  • Revised Date: October 23, 2019
  • The accuracy of the diagnosis of degraded insulators is improved to accurately identify low-zero-value insulators in the power grid. A pair of insulator infrared images and a gray wolf optimizer (GWO) optimized binary support vector machine (SVM) classifier is proposed. Low-zero insulators are detected automatically. First, the infrared image of the insulator is enhanced; then, the infrared image is segmented using the Ostu algorithm; and the obtained binary image is subjected to tilt angle correction and cutting to extract the effective region of the insulator string. Finally, the image features are applied to the classification and recognition of vector machines. The experimental results show that the GWO-SVM can identify the low-zero insulator more accurately and effectively than the commonly used grid search (GS) and particle swarm optimization (PSO). Its rate is higher.
  • [1]
    Lopes F V, Dantas K M, Silva K M, et al. Accurate two-terminal transmission line fault location using traveling waves[J]. IEEE Transactions on Power Delivery, 2018, 33(2): 873-880. DOI: 10.1109/TPWRD.2017.2711262
    [2]
    Costa F B, Monti A, Lopes F V, et al. Two-terminal traveling wave-based transmission line protection[J]. IEEE Transactions on Power Delivery, 2017, 32(3): 1382-1393. DOI: 10.1109/TPWRD.2016.2574900
    [3]
    CHENG Li, ZHANG Zhonghao, ZHANG Fuzeng, et al. Study on electrical properties and field solutions of water related heating of composite insulators on 500 kV AC transmission lines[C]//Electrical Insulation and Dielectric Phenomena IEEE, 2015: DOI: 10.1109/CEIDP. 2015.7352019.
    [4]
    吕玉坤, 赵伟萍, 庞广陆, 等. 典型伞型瓷及复合绝缘子积污特性模拟研究[J]. 电工技术学报, 2018, 33(1): 209-216. https://www.cnki.com.cn/Article/CJFDTOTAL-DGJS201801024.htm

    LYU Yukun, ZHAO Weiping, PANG Guanglu, et al. Simulation of contamination deposition on typical shed porcelain and composite insulators[J]. Transactions of China Electrotechnical Society, 2018, 33(1): 209-216. https://www.cnki.com.cn/Article/CJFDTOTAL-DGJS201801024.htm
    [5]
    律方成, 刘宏宇, 汪佛池, 等. 高速气流条件下污秽颗粒在复合绝缘子表面的沉积判据[J]. 电工技术学报, 2017, 32(1): 206-213. https://www.cnki.com.cn/Article/CJFDTOTAL-DGJS201701023.htm

    LYU Fangcheng, LIU Hongyu, WANG Fochi, et al. Deposit criterion of pollution particles on composite insulators surface under high speed aerosol[J]. Transactions of China Electrotechnical Society, 2017, 32(1): 206-213. https://www.cnki.com.cn/Article/CJFDTOTAL-DGJS201701023.htm
    [6]
    司马文霞, 施健, 袁涛, 等. 特高压复合绝缘子电场计算及基于神经网络遗传算法的均压环结构优化设计[J]. 高电压技术, 2012, 38(2): 257-265. https://www.cnki.com.cn/Article/CJFDTOTAL-GDYJ201202002.htm

    SIMA Wenxia, SHI Jian, YUAN Tao, et al. Electric field calculation of ultra high voltage composite insulator and optimization design of corona ring structure based on neural network and genetic algorithm[J]. High Voltage Engineering, 2012, 38(2) : 257-265. https://www.cnki.com.cn/Article/CJFDTOTAL-GDYJ201202002.htm
    [7]
    吴晓辉, 刘炯, 梁永春, 等. 支持向量机在电力变压器故障诊断中的应用[J]. 西安交通大学学报, 2007, 41(6): 457-457. https://www.cnki.com.cn/Article/CJFDTOTAL-XAJT200706020.htm

    WU Xiaohui, LIU Jiong, LIANG Yongchun, et al. Application of support vector machine in transformer fault diagnosis[J]. Journal of Xi'an Jiaotong University, 2007, 41(6): 457-457. https://www.cnki.com.cn/Article/CJFDTOTAL-XAJT200706020.htm
    [8]
    薛浩然, 张珂珩, 李斌, 等. 基于布谷鸟算法和支持向量机的变压器故障诊断[J]. 电力系统保护与控制, 2015, 43(8): 8-13. https://www.cnki.com.cn/Article/CJFDTOTAL-JDQW201508002.htm

    XUE Haoran, ZHANG Kehang, LI Bin, et al. Fault diagnosis of transformer based on the cuckoo search and support vector machine[J]. Power System Protection and Control, 2015, 43(8): 8-13 https://www.cnki.com.cn/Article/CJFDTOTAL-JDQW201508002.htm
    [9]
    张青, 赵黎明, 焦尚彬. 基于PSO-SVM的高压绝缘子污秽等级评定[J]. 高压电器, 2008, 44(6): 562-565. https://www.cnki.com.cn/Article/CJFDTOTAL-GYDQ200806023.htm

    ZHANG Qing, ZHAO Liming, JIAO Shangbin. Assessment of contamination grades for high voltage insulator based on PSO-SVM[J]. High Voltage Apparatus, 2008, 44(6): 562-565. https://www.cnki.com.cn/Article/CJFDTOTAL-GYDQ200806023.htm
    [10]
    刘颖, 陈谨女. 自适应中值滤波算法在图像处理中的应用[J]. 物联网技术, 2013, 3(3): 51-52. https://www.cnki.com.cn/Article/CJFDTOTAL-WLWJ201303022.htm

    LIU Ying, CHEN Jinnv. Application of adaptive median filtering algorithm in image processing[J]. Internet of Things Technologies, 2013, 3(3): 51-52. https://www.cnki.com.cn/Article/CJFDTOTAL-WLWJ201303022.htm
    [11]
    肖蕾, 何坤, 周激流, 等. 改进自适应中值滤波的图像去噪[J]. 激光杂志, 2009(2): 44-46. DOI: 10.3969/j.issn.0253-2743.2009.02.019

    XIAO Lei, HE Kun, ZHOU Jiliu, et al. Image noise removal on improvement adaptive medium filter[J]. Laser Journal, 2009(2): 44-46. DOI: 10.3969/j.issn.0253-2743.2009.02.019
    [12]
    宁春玉, 赵春华. 自适应中值滤波算法滤除医学图像脉冲噪声[J]. 计算机工程与应用, 2012, 48(24): 153-156. https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG201224034.htm

    NING Chunyu, ZHAO Chunhua. Removing impulse noise in medical images using adaptive median filtering algorithm[J]. Computer Engineering and Applications, 2012, 48(24): 153-156. https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG201224034.htm
    [13]
    杨新华, 吕意飞. 差分算子和改进Otsu算法结合的灰度图像阈值分割研究与实现[J]. 仪表技术与传感器, 2015(3): 32. https://www.cnki.com.cn/Article/CJFDTOTAL-YBJS201503032.htm

    YANG Xinhua, LV Yifei. Research and implementation of grayscale image threshold segmentation based on difference operators combined with instrument technique and sensor improved Otsu algorithm[J]. Instrument Technique and Sensor, 2015(3): 32. https://www.cnki.com.cn/Article/CJFDTOTAL-YBJS201503032.htm
    [14]
    方梓涵, 张焕明, 朱家明. 基于RADON逆变换对CT系统参数标定及成像分析[J]. 哈尔滨师范大学自然科学学报, 2018(2): 32. https://www.cnki.com.cn/Article/CJFDTOTAL-HEBY201802005.htm

    FANG Zihan, ZHANG Huanming, ZHU Jiaming. Parameter calibration and imaging analysis of CT system based on RADON inverse transform[J]. Natural Science Journal of Harbin Normal University, 2018(2): 32. https://www.cnki.com.cn/Article/CJFDTOTAL-HEBY201802005.htm
    [15]
    杨凯, 张认成, 杨建红, 等. 基于分形维数和支持向量机的串联电弧故障诊断方法[J]. 电工技术学报, 2016, 31(2): 70-77. https://www.cnki.com.cn/Article/CJFDTOTAL-DGJS201602011.htm

    YANG Kai, ZHANG Rencheng, YANG Jianhong, et al. Series are fault diagnostic method based on fractal dimension and support vector machine[J]. Transactions of China Electrotechnical Society, 2016, 31(2): 70-77. https://www.cnki.com.cn/Article/CJFDTOTAL-DGJS201602011.htm
    [16]
    MIRJALILI S, MIRJALILI S M, LEWIS A. Grey Wolf Optimizer[J]. Advances in Engineering Solfware, 2014, 69(3): 46-61. http://www.sciencedirect.com/science/article/pii/s0965997813001853
  • Related Articles

    [1]DING Zitian, XI Wenfei, QIAN Tanghui, GUO Junqi, JIN Tingting, HONG Wenyu, GUI Fuyu. Multiple Feature Fusion for Unmanned Aerial Vehicle Image Recognition in Foggy Weather[J]. Infrared Technology , 2025, 47(7): 833-841.
    [2]CHU Hongjia, CHEN Guanghua, WANG Kaixuan. Fast Finger Vein Recognition Based on a Dual Dimension Reduction Histogram of Oriented Gradient and Support Vector Machine[J]. Infrared Technology , 2022, 44(3): 262-267.
    [3]SONG Shanshan, ZHAI Xuping. Improved Infrared Anomaly Target Detection Algorithm Based on Single Gaussian Model[J]. Infrared Technology , 2021, 43(9): 885-888,894.
    [4]WANG Zhouchun, CUI Wennan, ZHANG Tao. Classification and Recognition Algorithm for Long-wave Infrared Targets Based on Support Vector Machine[J]. Infrared Technology , 2021, 43(2): 153-161.
    [5]SUN Zhong-hua, YANG Xiao-di, GuLimila?kezierbieke. A Multi-scale Wavelet Image Retrieval Simulation of Kernel Extreme Learning Machine[J]. Infrared Technology , 2015, (6): 484-487.
    [6]JING Yuan-yuan, TIAN Yuan. Image Segmentation Research Based on Kernel Function of Support Vector Machine Algorithm[J]. Infrared Technology , 2015, (3): 234-239.
    [7]ZHANG Su-wen, CHU Nai-qiang. A Filtering Algorithm Based on Grey Correlative Degree for Salt-Pepper Noise Images[J]. Infrared Technology , 2008, 30(11): 651-654. DOI: 10.3969/j.issn.1001-8891.2008.11.008
    [8]FAN Bin, FENG Yun-Song. The Application of the Support Vector Machine in Infared Imaging Automatic Target Recognition[J]. Infrared Technology , 2007, 29(1): 38-41. DOI: 10.3969/j.issn.1001-8891.2007.01.010
    [9]FENG Dong-zhu, YAN Jie. An Algorithm of Infrared Imaging Edge Detection Based on Grey Relational Analysis[J]. Infrared Technology , 2006, 28(3): 161-164. DOI: 10.3969/j.issn.1001-8891.2006.03.010
    [10]Analysis of Infrared Image Tracking Algorithms Performance and Application[J]. Infrared Technology , 2004, 26(4): 11-15,19. DOI: 10.3969/j.issn.1001-8891.2004.04.003

Catalog

    Article views (410) PDF downloads (34) Cited by()
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return