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Chongqing","award":["KJQN201801516"],"award-info":[{"award-number":["KJQN201801516"]}]},{"name":"Natural Science Foundation of Chongqing","award":["KJQN201901536"],"award-info":[{"award-number":["KJQN201901536"]}]},{"name":"Natural Science Foundation of Chongqing","award":["KJQN202001531"],"award-info":[{"award-number":["KJQN202001531"]}]},{"name":"Natural Science Foundation of Chongqing","award":["cstc2018jcyjAX0336"],"award-info":[{"award-number":["cstc2018jcyjAX0336"]}]},{"name":"Natural Science Foundation of Chongqing","award":["YKJCX2020403"],"award-info":[{"award-number":["YKJCX2020403"]}]},{"name":"Natural Science Foundation of Chongqing","award":["YKJCX2020402"],"award-info":[{"award-number":["YKJCX2020402"]}]},{"name":"Chongqing University of Science and Technology","award":["61873043"],"award-info":[{"award-number":["61873043"]}]},{"name":"Chongqing University of Science and Technology","award":["61903054"],"award-info":[{"award-number":["61903054"]}]},{"name":"Chongqing University of Science and Technology","award":["KJQN201801516"],"award-info":[{"award-number":["KJQN201801516"]}]},{"name":"Chongqing University of Science and Technology","award":["KJQN201901536"],"award-info":[{"award-number":["KJQN201901536"]}]},{"name":"Chongqing University of Science and Technology","award":["KJQN202001531"],"award-info":[{"award-number":["KJQN202001531"]}]},{"name":"Chongqing University of Science and Technology","award":["cstc2018jcyjAX0336"],"award-info":[{"award-number":["cstc2018jcyjAX0336"]}]},{"name":"Chongqing University of Science and Technology","award":["YKJCX2020403"],"award-info":[{"award-number":["YKJCX2020403"]}]},{"name":"Chongqing University of Science and Technology","award":["YKJCX2020402"],"award-info":[{"award-number":["YKJCX2020402"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The failure of insulators may seriously threaten the safe operation of the power system, where the state detection of high-voltage insulators is a must for the normal and safe operation of the power system. Based on the data of insulators in aerial images, this work explored an enhanced particle swarm algorithm to optimize the parameters of the support vector machine. A support vector machine model was therefore established for the identification of the normal and defective states of the insulators. This methodology works with the structure minimization principle of SVM and the characteristics of particle swarm fast optimization. First, the aerial insulator image was segmented as a target by way of the seed region growth based on double-layer cascade morphological improvements, and then, HOG features plus GLCM features were extracted as sample data. Finally, an ameliorated PSO-SVM classifier was designed to realize insulator state identification. Comparisons were made between PSO-SVM and conventional machine learning algorithms, SVM and Random Forest, and an optimization algorithm, Gray Wolf Optimization Support Vector Machine (GWO-SVM), and advanced neural network CNN. The experimental results showed that the performance of the algorithm proposed in this paper touched the top level, where the recognition accuracy rate was 92.11%, the precision rate 90%, the recall rate 94.74%, and the F1-score 92.31%.<\/jats:p>","DOI":"10.3390\/s23010272","type":"journal-article","created":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T05:31:54Z","timestamp":1672205514000},"page":"272","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["A New Approach to Optimize SVM for Insulator State Identification Based on Improved PSO Algorithm"],"prefix":"10.3390","volume":"23","author":[{"given":"Lepeng","family":"Song","sequence":"first","affiliation":[{"name":"School of Electrical Engineering, Chongqing University of Science and Technology, Chongqing 401331, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qin","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Chongqing University of Science and Technology, Chongqing 401331, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Chongqing University of Science and Technology, Chongqing 401331, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Hu","sequence":"additional","affiliation":[{"name":"The School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Chongqing University of Science and Technology, Chongqing 401331, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanling","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Chongqing University of Science and Technology, Chongqing 401331, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Azevedo, F., Dias, A., Almeida, J., Oliveira, A., Ferreira, A., Santos, T., Martins, A., and Silva, E. (2019). LiDAR-Based Real-Time Detection and Modeling of Power Lines for Unmanned Aerial Vehicles. Sensors, 19.","DOI":"10.3390\/s19081812"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ma, Y., Li, Q., Chu, L., Zhou, Y., and Xu, C. (2021). Real-Time Detection and Spatial Localization of Insulators for UAV Inspection Based on Binocular Stereo Vision. Remote Sens., 13.","DOI":"10.3390\/rs13020230"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"105862","DOI":"10.1016\/j.ijepes.2020.105862","article-title":"Power transmission line inspection robots: A review, trends and challenges for future research","volume":"118","author":"Alhassan","year":"2020","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Lopez Lopez, R., Batista Sanchez, M.J., Perez Jimenez, M., Arrue, B.C., and Ollero, A. (2021). Autonomous UAV System for Cleaning Insulators in Power Line Inspection and Maintenance. Sensors, 21.","DOI":"10.3390\/s21248488"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"115","DOI":"10.25103\/jestr.104.16","article-title":"Image Segmentation Method of Insulator in Transmission Line Based on Weighted Variable Fuzzy C-Means","volume":"10","author":"Ke","year":"2017","journal-title":"J. Eng. Sci. Technol. Rev."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1509","DOI":"10.1109\/JSTARS.2012.2197672","article-title":"A texture segmentation algorithm based on PCA and global minimization active contour model for aerial insulator images","volume":"5","author":"Wu","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"78706","DOI":"10.1109\/ACCESS.2019.2922257","article-title":"Texture-and-Shape Based Active Contour Model for Insulator Segmentation","volume":"7","author":"Yu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"32728","DOI":"10.1109\/ACCESS.2019.2900658","article-title":"Edge Detection of High-Voltage Porcelain Insulators in Infrared Image Using Dual Parity Morphological Gradients","volume":"7","author":"Yin","year":"2019","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5345","DOI":"10.1109\/TIM.2020.2965635","article-title":"Automatic Fault Diagnosis of Infrared Insulator Images Based on Image Instance Segmentation and Temperature Analysis","volume":"69","author":"Wang","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2468431","DOI":"10.1155\/2022\/2468431","article-title":"Insulator Semantic Segmentation in Aerial Images Based on Multiscale Feature Fusion","volume":"2022","author":"Cui","year":"2022","journal-title":"Complexity"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1016\/j.compeleceng.2019.08.001","article-title":"Insulator visual non-conformity detection in overhead power distribution lines using deep learning","volume":"78","author":"Prates","year":"2019","journal-title":"Comput. Electr. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"97830","DOI":"10.1109\/ACCESS.2020.2995910","article-title":"An Improved AlexNet for Power Edge Transmission Line Anomaly Detection","volume":"8","author":"Guo","year":"2020","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"61797","DOI":"10.1109\/ACCESS.2019.2915985","article-title":"Insulator Fault Detection in Aerial Images Based on Ensemble Learning with Multi-Level Perception","volume":"7","author":"Jiang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"38448","DOI":"10.1109\/ACCESS.2020.2974798","article-title":"Detection and Evaluation Method of Transmission Line Defects Based on Deep Learning","volume":"8","author":"Liang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wen, Q., Luo, Z., Chen, R., Yang, Y., and Li, G. (2021). Deep Learning Approaches on Defect Detection in High Resolution Aerial Images of Insulators. Sensors, 21.","DOI":"10.3390\/s21041033"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"59934","DOI":"10.1109\/ACCESS.2020.2982288","article-title":"Insulator Defect Recognition Based on Global Detection and Local Segmentation","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Huang, S., Li, Y., Li, H., and Hao, H. (2022). Image Detection of Insulator Defects Based on Morphological Processing and Deep Learning. Energies, 15.","DOI":"10.3390\/en15072465"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"164214","DOI":"10.1109\/ACCESS.2020.3022419","article-title":"An Automatic Detection Method of Bird\u2019s Nest on Transmission Line Tower Based on Faster-RCNN","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Han, J., Yang, Z., Xu, H., Hu, G., Zhang, C., Li, H., Lai, S., and Zeng, H. (2020). Search Like an Eagle: A Cascaded Model for Insulator Missing Faults Detection in Aerial Images. Energies, 13.","DOI":"10.3390\/en13030713"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liu, C., Wu, Y., Liu, J., Sun, Z., and Xu, H. (2021). Insulator Faults Detection in Aerial Images from High-Voltage Transmission Lines Based on Deep Learning Model. Appl. Sci., 11.","DOI":"10.3390\/app11104647"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yu, Y., Qiu, Z., Liao, H., Wei, Z., Zhu, X., and Zhou, Z. (2022). A Method Based on Multi-Network Feature Fusion and Random Forest for Foreign Objects Detection on Transmission Lines. Appl. Sci., 12.","DOI":"10.3390\/app12104982"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Stefenon, S.F., Singh, G., Yow, K.-C., and Cimatti, A. (2022). Semi-ProtoPNet Deep Neural Network for the Classification of Defective Power Grid Distribution Structures. Sensors, 22.","DOI":"10.3390\/s22134859"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"107336","DOI":"10.1016\/j.ijepes.2021.107336","article-title":"Echo state network applied for classification of medium voltage insulators","volume":"134","author":"Stefenon","year":"2020","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1049\/gtd2.12353","article-title":"Classification of insulators using neural network based on computer vision","volume":"16","author":"Stefenon","year":"2021","journal-title":"IET Gener. Transm. Distrib."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"101283","DOI":"10.1109\/ACCESS.2019.2931144","article-title":"Deep Learning-Based System for Automatic Recognition and Diagnosis of Electrical Insulator Strings","volume":"7","author":"Sampedro","year":"2019","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1486","DOI":"10.1109\/TSMC.2018.2871750","article-title":"Detection of Power Line Insulator Defects Using Aerial Images Analyzed with Convolutional Neural Networks","volume":"50","author":"Tao","year":"2020","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_27","first-page":"108","article-title":"Power Cable Fault Classification Using Improved Bayesian Decision Algorithm","volume":"4","author":"Wang","year":"2012","journal-title":"Int. J. Adv. Comput. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1049\/hve.2019.0079","article-title":"High voltage outdoor insulator surface condition evaluation using aerial insulator images","volume":"4","author":"Pernebayeva","year":"2019","journal-title":"High Volt."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Kundu, S., Malakar, S., Geem, Z.W., Moon, Y.Y., Singh, P.K., and Sarkar, R. (2021). Hough Transform-Based Angular Features for Learning-Free Handwritten Keyword Spotting. Sensors, 21.","DOI":"10.3390\/s21144648"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kang, Z., Huang, T., Zeng, S., Li, H., Dong, L., and Zhang, C. (2022). A Method for Detection of Corn Kernel Mildew Based on Co-Clustering Algorithm with Hyperspectral Image Technology. Sensors, 22.","DOI":"10.3390\/s22145333"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Corso, M.P., Perez, F.L., Stefenon, S.F., Yow, K.C., Garc\u00eda Ovejero, R., and Leithardt, V.R.Q. (2021). Classification of Contaminated Insulators Using k-Nearest Neighbors Based on Computer Vision. Computers, 10.","DOI":"10.20944\/preprints202108.0282.v1"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yao, W., Xu, Y., Qian, Y., Sheng, G., and Jiang, X. (2020). A Classification System for Insulation Defect Identification of Gas-Insulated Switchgear (GIS), Based on Voiceprint Recognition Technology. Appl. Sci., 10.","DOI":"10.3390\/app10113995"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Sun, R., Qin, G., Li, G., Hu, J., Xiong, J., and Xu, H. (2022). Abnormal Conductive State Identification of the Copper Rod in a Nickel Electrolysis Procedure Based on Infrared Image Features and Position Characteristics. Appl. Sci., 12.","DOI":"10.3390\/app12073691"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1109\/TDEI.2010.5412006","article-title":"Insulator condition analysis for overhead distribution lines using combined wavelet support vector machine (SVM)","volume":"17","author":"Murthy","year":"2010","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"664","DOI":"10.1109\/TDEI.2013.6508770","article-title":"Condition monitoring of 11 kV distribution system insulators incorporating complex imagery using combined DOST-SVM approach","volume":"20","author":"Reddy","year":"2013","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_36","unstructured":"Yan, T., Yang, G., and Yu, J. (2017, January 26\u201328). Feature fusion-based insulator detection for aerial inspection. Proceedings of the 2017 36th Chinese Control Conference (CCC), Dalian, China."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1049\/hve2.12019","article-title":"Contamination degree prediction of insulator surface based on exploratory factor analysis-least square support vector machine combined model","volume":"6","author":"Sun","year":"2021","journal-title":"High Volt."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"986","DOI":"10.1049\/hve2.12081","article-title":"Classification of partial discharge severities of ceramic insulators based on texture analysis of UV pulses","volume":"6","author":"Ma","year":"2021","journal-title":"High Volt."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2858","DOI":"10.1109\/TDEI.2016.7736846","article-title":"Representation of binary feature pooling for detection of insulator strings in infrared images","volume":"23","author":"Zhao","year":"2016","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"21831","DOI":"10.1109\/ACCESS.2017.2757030","article-title":"Aggregating Deep Convolutional Feature Maps for Insulator Detection in Infrared Images","volume":"5","author":"Zhao","year":"2017","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.ultras.2018.08.014","article-title":"Classification of spot-welded joint strength using ultrasonic signal time-frequency features and PSO-SVM method","volume":"91","author":"Wang","year":"2019","journal-title":"Ultrasonics"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1109\/TIM.2019.2895482","article-title":"Insulation Defect Diagnostic Method for OIP Bushing Based on Multiclass LS-SVM and Cuckoo Search","volume":"69","author":"Wang","year":"2020","journal-title":"IEEE Trans Instrum. Meas."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Cho, B.-H., Kim, Y.-H., Lee, K.-B., Hong, Y.-K., and Kim, K.-C. (2022). Potential of Snapshot-Type Hyperspectral Imagery Using Support Vector Classifier for the Classification of Tomatoes Maturity. Sensors, 22.","DOI":"10.3390\/s22124378"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Liu, B., and Shi, Q. (2020, January 20\u201323). Energy Entropy Feature and Diagnosis of Partial Discharge Wavelet Packet in GIS Based on Support Vector Machine. Proceedings of the 2020 12th IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), Nanjing, China.","DOI":"10.1109\/APPEEC48164.2020.9220524"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Van, M., Hoang, D.T., and Kang, H.J. (2020). Bearing Fault Diagnosis Using a Particle Swarm Optimization-Least Squares Wavelet Support Vector Machine Classifier. Sensors, 20.","DOI":"10.3390\/s20123422"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Chen, H., and Li, S. (2022). Multi-Sensor Fusion by CWT-PARAFAC-IPSO-SVM for Intelligent Mechanical Fault Diagnosis. Sensors, 22.","DOI":"10.3390\/s22103647"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Li, N., Zhu, L., Ma, W., Wang, Y., He, F., Zheng, A., and Zhang, X. (2022). The Identification of ECG Signals Using WT-UKF and IPSO-SVM. Sensors, 22.","DOI":"10.3390\/s22051962"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Moon, H.G., Jung, Y., Shin, B., Lee, D., Kim, K., Woo, D.H., Lee, S., Kim, S., Kang, C.-Y., and Lee, T. (2022). Identification of Chemical Vapor Mixture Assisted by Artificially Extended Database for Environmental Monitoring. Sensors, 22.","DOI":"10.3390\/s22031169"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Li, X., Ba, Y., Zhang, M., Nong, M., Yang, C., and Zhang, S. (2022). Sugarcane Nitrogen Concentration and Irrigation Level Prediction Based on UAV Multispectral Imagery. Sensors, 22.","DOI":"10.3390\/s22072711"},{"key":"ref_50","first-page":"1","article-title":"HOG-ShipCLSNet: A Novel Deep Learning Network with HOG Feature Fusion for SAR Ship Classification","volume":"60","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"656","DOI":"10.1109\/TITS.2013.2284666","article-title":"An Efficient Hardware Implementation of HOG Feature Extraction for Human Detection","volume":"15","author":"Chen","year":"2014","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"29306","DOI":"10.1109\/ACCESS.2018.2813395","article-title":"The excellent properties of a dense grid-based HOG feature on face recognition compared to Gabor and LBP","volume":"6","author":"Xiang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.eswa.2017.05.033","article-title":"Improving handwriting based gender classification using ensemble classifiers","volume":"85","author":"Ahmed","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/s11721-007-0002-0","article-title":"Particle swarm optimization","volume":"1","author":"Poli","year":"2007","journal-title":"Swarm Intell."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/1\/272\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:52:31Z","timestamp":1760147551000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/1\/272"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,27]]},"references-count":54,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["s23010272"],"URL":"https:\/\/doi.org\/10.3390\/s23010272","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,27]]}}}