{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T04:38:49Z","timestamp":1770352729480,"version":"3.49.0"},"reference-count":33,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,9,25]],"date-time":"2024-09-25T00:00:00Z","timestamp":1727222400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Management Technology Project of State Grid Liaoning Electric Power Co., LTD.","award":["2023ZX-05"],"award-info":[{"award-number":["2023ZX-05"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>As more and more transmission lines need to pass through areas with heavy icing, the problem of transmission line faults caused by ice and snow disasters frequently occurs. Existing ice coverage monitoring methods have defects such as the use of a single monitoring type, low accuracy of monitoring results, and an inability to obtain ice coverage data over time. Therefore, this study proposes a new algorithm for detecting the icing status of transmission lines. The algorithm uses two-dimensional multifractal detrended fluctuation analysis (2D MF-DFA) to determine the optimal sliding-window size and wave function and accurately segment and extract local feature areas. Based on the local Hurst exponent (Lh(z)) and the power-law relationship between the fluctuation function and the scale at multiple continuous scales, the ice-covered area of a transmission conductor was accurately detected. By analyzing and calculating the key target pixels, the icing thickness was accurately measured, achieving accurate detection of the icing status of the transmission lines. The experimental results show that this method can accurately detect ice-covered areas and the icing thickness of transmission lines under various working conditions, providing a strong guarantee for the safe and reliable operation of transmission lines under severe weather conditions.<\/jats:p>","DOI":"10.3390\/sym16101264","type":"journal-article","created":{"date-parts":[[2024,9,26]],"date-time":"2024-09-26T04:05:46Z","timestamp":1727323546000},"page":"1264","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Novel Detection Algorithm for the Icing Status of Transmission Lines"],"prefix":"10.3390","volume":"16","author":[{"given":"Dongxu","family":"Dai","sequence":"first","affiliation":[{"name":"State Grid Liaoning Province Electric Power Co., LTD. Benxi Power Supply Company, Benxi 117000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Hu","sequence":"additional","affiliation":[{"name":"State Grid Liaoning Province Electric Power Co., LTD. Benxi Power Supply Company, Benxi 117000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Qian","sequence":"additional","affiliation":[{"name":"State Grid Liaoning Province Electric Power Co., LTD. Benxi Power Supply Company, Benxi 117000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoqiang","family":"Qi","sequence":"additional","affiliation":[{"name":"State Grid Liaoning Province Electric Power Co., LTD. Benxi Power Supply Company, Benxi 117000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Wang","sequence":"additional","affiliation":[{"name":"State Grid Liaoning Province Electric Power Co., LTD. Benxi Power Supply Company, Benxi 117000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"025302","DOI":"10.1088\/2631-8695\/ad3711","article-title":"Fault distance measurement method for wind power transmission lines based on improved NSGA II","volume":"6","author":"Qi","year":"2024","journal-title":"Eng. Res. Express"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"998","DOI":"10.1109\/JPROC.2011.2109670","article-title":"For the grid and through the grid: The role of power line communications in the smart grid","volume":"99","author":"Galli","year":"2011","journal-title":"Proc. IEEE"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Huang, J., Yang, H., and Wang, Y. (2015, January 26\u201329). Forecast of line ice-coating degree using circumfluence index & support vector machine method. Proceedings of the 2015 5th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT), Changsha, China.","DOI":"10.1109\/DRPT.2015.7432719"},{"key":"ref_4","first-page":"21892203","article-title":"Modeling wet snow accretion on power lines: Improvements to previous methods using 50 years of observations","volume":"52","author":"Nygaard","year":"2013","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2419","DOI":"10.5194\/nhess-11-2419-2011","article-title":"Wet snow hazard for power lines: A forecast and alert system applied in Italy","volume":"11","author":"Bonelli","year":"2011","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Xu, X., Niu, D., Zhang, L., Wang, Y., and Wang, K. (2017). Ice cover prediction of a power grid transmission line based on two-stage data processing and adaptive support vector machine optimized by genetic tabu search. Energies, 10.","DOI":"10.3390\/en10111862"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1997","DOI":"10.1109\/TDEI.2014.004598","article-title":"Insulator flashover under icing conditions","volume":"21","author":"Farzaneh","year":"2014","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Xu, Z., Xu, T., Yao, T., Li, X., Li, J., Chen, J., Cai, L., and Jia, R. (2014, January 19\u201322). Flashover performance of UHV & EHV post insulatros under icing conditions. Proceedings of the 2014 IEEE Conference on Electrical Insulation and Dielectric Phenomena (CEIDP), Des Moines, IA, USA.","DOI":"10.1109\/CEIDP.2014.6995754"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"665","DOI":"10.32604\/ee.2023.020342","article-title":"Review of Optical Character Recognition for Power System Image Based on Artificial Intelligence Algorithm","volume":"120","author":"Zhang","year":"2023","journal-title":"Energy Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1867","DOI":"10.32604\/ee.2023.028453","article-title":"Fault Diagnosis of Industrial Motors with Extremely Similar Thermal Images Based on Deep Learning-Related Classification Approaches","volume":"120","author":"Zhang","year":"2023","journal-title":"Energy Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"895","DOI":"10.32604\/ee.2023.041002","article-title":"Study on Image Recognition Algorithm for Residual Snow and Ice on Photovoltaic Modules","volume":"121","author":"Zhu","year":"2024","journal-title":"Energy Eng."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wang, J., Wang, J., Shao, J., and Li, J. (2017). Image recognition of icing thickness on power transmission lines based on a least squares Hough transform. Energies, 10.","DOI":"10.3390\/en10040415"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"40695","DOI":"10.1109\/ACCESS.2019.2907635","article-title":"Transmission line ice coating prediction model based on EEMD feature extraction","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Cheng, X., Wang, X., Zhang, P., and Liu, W. (2018, January 25\u201327). Ice Detection of Transmission Line Based on Image Fusion. Proceedings of the 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), Xi\u2019an, China.","DOI":"10.1109\/IMCEC.2018.8469319"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yan, G., Wei, C., Jia, X., Li, Y., and Chang, W. (2024). MAS-Net: Multi-Attention Hybrid Network for Superpixel Segmentation. Symmetry, 16.","DOI":"10.3390\/sym16081000"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, W., Xu, X., and Yang, H. (2024). Intelligent Detection of Tunnel Leakage Based on Improved Mask R-CNN. Symmetry, 16.","DOI":"10.3390\/sym16060709"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Su, B., and Xu, G. (2019, January 18\u201320). Automatic detection method for iced transmission lines under complex background. Proceedings of the 2019 3rd International Conference on Electronic Information Technology and Computer Engineering (EITCE), Xiamen, China.","DOI":"10.1109\/EITCE47263.2019.9095127"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, W., Yousaf, M., Liu, D., and Sohail, A. (2022). A comparative study of the genetic deep learning image segmentation algorithms. Symmetry, 14.","DOI":"10.3390\/sym14101977"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wang, X., Hu, J., Wu, B., Du, L., and Sun, C. (2018, January 9\u201313). Study on edge extraction methods for image-based icing on-line monitoring on overhead transmission lines. Proceedings of the 2008 International Conference on High Voltage Engineering and Application, Chongqing, China.","DOI":"10.1109\/ICHVE.2008.4774022"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Bao, X., Jia, H., and Lang, C. (2019). Dragonfly algorithm with opposition-based learning for multilevel thresholding color image segmentation. Symmetry, 11.","DOI":"10.3390\/sym11050716"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1783","DOI":"10.1007\/s11036-023-02244-1","article-title":"Image Identification Method of Ice Thickness on Transmission Line Based on Visual Sensing","volume":"28","author":"Hu","year":"2023","journal-title":"Mob. Netw. Appl."},{"key":"ref_22","unstructured":"Zhong, Y.P., Zuo, Q., Zhou, Y., and Zhang, C. (2013, January 15\u201319). A new image-based algorithm for icing detection and icing thickness estimation for transmission lines. Proceedings of the 2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), San Jose, CA, USA."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"113744","DOI":"10.1016\/j.eswa.2020.113744","article-title":"Rotation invariant angle-density based features for an ice image classification system","volume":"162","author":"Yue","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"109405","DOI":"10.1016\/j.epsr.2023.109405","article-title":"Research on transmission line ice-cover segmentation based on improved U-Net and GAN","volume":"221","author":"Hu","year":"2023","journal-title":"Electr. Power Syst. Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1109\/TDEI.2016.006049","article-title":"Recognition of natural ice types on in-service glass insulators based on texture feature descriptor","volume":"24","author":"Yang","year":"2017","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Huang, X., and Wei, X. (2012, January 23\u201327). A new on-line monitoring technology of transmission line conductor icing. Proceedings of the 2012 IEEE International Conference on Condition Monitoring and Diagnosis, Bali, Indonesia.","DOI":"10.1109\/CMD.2012.6416211"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/S0378-4371(02)01383-3","article-title":"Multifractal detrended fluctuation analysis of nonstationary time series","volume":"316","author":"Kantelhardt","year":"2002","journal-title":"Phys. A"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"420","DOI":"10.18517\/ijaseit.10.2.11011","article-title":"Medical image segmentation using multifractal analysis","volume":"10","author":"Alshehri","year":"2020","journal-title":"Int. J. Adv. Sci. Eng. Inf. Technol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"980","DOI":"10.1109\/TGRS.2002.1006395","article-title":"A novel multifractal estimation method and its application to remote image segmentation","volume":"40","author":"Du","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"214905","DOI":"10.1063\/1.4839815","article-title":"Leaf image segmentation method based on multifractal detrended fluctuation analysis","volume":"114","author":"Wang","year":"2013","journal-title":"J. Appl. Phys."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Korchiyne, R., Sbihi, A., Farssi, S.M., Touahni, R., and Alaoui, M.T. (2012, January 10\u201312). Medical image texture segmentation using multifractal analysis. Proceedings of the 2012 International Conference on Multimedia Computing and Systems, Tangiers, Morocco.","DOI":"10.1109\/ICMCS.2012.6320316"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.patrec.2009.09.028","article-title":"Multifractal signature estimation for textured image segmentation","volume":"31","author":"Xia","year":"2010","journal-title":"Pattern Recognit. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"109794","DOI":"10.1016\/j.epsr.2023.109794","article-title":"Research on sag monitoring of ice-accreted transmission line arcs based on stereovision technique","volume":"225","author":"Wang","year":"2023","journal-title":"Electr. Power Syst. Res."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/16\/10\/1264\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:03:03Z","timestamp":1760112183000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/16\/10\/1264"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,25]]},"references-count":33,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["sym16101264"],"URL":"https:\/\/doi.org\/10.3390\/sym16101264","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,25]]}}}