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Elektrik İletim ve Dağıtım Hatlarındaki İzolatör Tespiti ve Sınıflandırılması Detection and Classification of Insulators in Electrical Transmission and Distribution Lines

Yıl 2026, Cilt: 16 Sayı: 1, 21 - 31, 31.01.2026

Öz

Elektrik enerji iletim hatlarında güvenliğin ve sürekliliğin sağlanmasında kritik bir rol oynayan izolatörler, dış ortam koşullarına sürekli maruz kalan yapıları nedeniyle zamanla çeşitli arızalara ve hasarlara açık hale gelmektedir. Bu bileşenlerde meydana gelen kırıklar, yüzeysel hasarlar ya da atlama gibi durumlar, sistemin genel performansını olumsuz etkileyebilmekte ve ciddi arızalara yol açabilmektedir. Bu nedenle izolatörlerin durumsal takibi ve arıza tespiti, elektrik iletim altyapısının sağlıklı çalışması açısından büyük önem taşımaktadır. Geleneksel yöntemlerle yapılan görsel kontrollerin zaman alıcı ve insan hatasına açık olması, son yıllarda derin öğrenme tabanlı görüntü işleme yöntemlerinin bu alanda kullanımını yaygınlaştırmıştır. Bu çalışmada, farklı derin öğrenme mimarilerine sahip nesne tespit modelleri kullanılarak, izolatör bileşenlerinin sınıflandırılması ve hasar tespiti üzerine kapsamlı bir karşılaştırmalı analiz gerçekleştirilmiştir. Çalışmada kullanılan modeller; kare başına tespit süresi, ortalama doğruluk ve diğer performans ölçütleri açısından, izolatör, iyi disk, kırık disk ve atlama yapmış disk sınıfları temelinde değerlendirilmiştir.

Kaynakça

  • [1] Han, S., Hao, R., and Lee, J.. “Inspection of Insulators on High-Voltage Power Transmission Lines,” IEEE Transactions on Power Delivery, vol. 24, no. 4, pp. 2319–2327, 2009, doi: 10.1109/TPWRD.2009.2028534.
  • [2] Anjum, S.. “A study of the detection of defects in ceramic insulators based on radio frequency signatures,” University of Waterloo, 2014.
  • [3] Vaillancourt, G. H., Bellerive, J. P., St-Jean, M., and Jean, C.. “New live line tester for porcelain suspension insulators on high-voltage power lines,” IEEE Transactions on Power Delivery, vol. 9, no. 1, pp. 208–219, Jan. 1994, doi: 10.1109/61.277692.
  • [4] Bretuj, W., Fleszynski, J., and Wieczorek, K.. “Diagnostyka izolatorów kompozytowych eksploatowanych w liniach elektroenergetycznych,” Przeglad Elektrotechniczny, vol. 88, no. 5a, pp. 51–54, 2012.
  • [5] Chojnacki, A.. “Analiza niezawodności wybranych urządzeń stacji transformatorowo-rozdzielczych SN/NN,” Energetyka, vol. 7, 2011.
  • [6] Tomaszewski, M., Gasz, R., Kasana, S. S., Osuchowski, J., Singh, S., and Zator, S.. “TCIP: Transformed Colour Intensity Profiles analysis for fault detection in power line insulators,” Multimed Tools Appl, Mar. 2024, doi: 10.1007/s11042-024-18901-w.
  • [7] Wang, X. and Zhang, Y.. “Insulator identification from aerial images using Support Vector Machine with background suppression,” in 2016 International Conference on Unmanned Aircraft Systems (ICUAS), Arlington, VA, USA: IEEE, Jun. 2016, pp. 892–897. doi: 10.1109/ICUAS.2016.7502544.
  • [8] Salustiano, R. et al.. “Development of new methodology for insulators inspections on aerial distribution lines based on partial discharge detection tools,” in 2014 ICHVE International Conference on High Voltage Engineering and Application, Poznan, Poland: IEEE, Sep. 2014, pp. 1–4. doi: 10.1109/ICHVE.2014.7035429.
  • [9] Lee, J.-K., Park, J.-Y., Cho, B.-H., and Oh, K.-Y.. “Development of inspection tool for live-line insulator strings in 154kV power transmission lines,” in 2009 13th European Conference on Power Electronics and Applications, 2009, pp. 1–8.
  • [10] Brito, K. B., Costa, E. G., Dias, B. A., Florentino, M. T. B., and Lira, G. R. S.. “Development of DIP-based algorithm for extraction of UV video attributes from corona discharges on polymeric insulators,” International Journal of Electrical Power & Energy Systems, vol. 134, p. 107406, 2022, doi: https://doi.org/10.1016/j.ijepes.2021.107406.
  • [11] Mirallès, F., Pouliot, N., and Montambault, S.. “State-of-the-art review of computer vision for the management of power transmission lines,” in Proceedings of the 2014 3rd International Conference on Applied Robotics for the Power Industry, 2014, pp. 1–6. doi: 10.1109/CARPI.2014.7030068.
  • [12] Sampedro, C., Rodriguez-Vazquez, J., Rodriguez-Ramos, A., Carrio, A., and Campoy, P.. “Deep Learning-Based System for Automatic Recognition and Diagnosis of Electrical Insulator Strings,” IEEE Access, vol. 7, pp. 101283 – 101308, 2019, doi: 10.1109/ACCESS.2019.2931144.
  • [13] Jiang, H., Qiu, X., Chen, J., Liu, X., Miao, X., and Zhuang, S.. “Insulator Fault Detection in Aerial Images Based on Ensemble Learning with Multi-Level Perception,” IEEE Access, vol. 7, pp. 61797 – 61810, 2019, doi: 10.1109/ACCESS.2019.2915985.
  • [14] Zhang, Y., Huang, X., Jia, J., and Liu, X.. “A Recognition Technology of Transmission Lines Conductor Break and Surface Damage Based on Aerial Image,” IEEE Access, vol. 7, pp. 59022–59036, 2019, doi: 10.1109/ACCESS.2019.2914766.
  • [15] Tao, X., Zhang, D., Wang, Z., Liu, X., Zhang, H., and Xu, D.. “Detection of Power Line Insulator Defects Using Aerial Images Analyzed With Convolutional Neural Networks,” IEEE Trans Syst Man Cybern Syst, vol. 50, no. 4, pp. 1486–1498, 2020, doi: 10.1109/TSMC.2018.2871750.
  • [16] Wen, Q., Luo, Z., Chen, R., Yang, Y., and Li, G.. “Deep Learning Approaches on Defect Detection in High Resolution Aerial Images of Insulators,” Sensors, vol. 21, no. 4, 2021, doi: 10.3390/s21041033.
  • [17] Li, X., Su, H., and Liu, G.. “Insulator Defect Recognition Based on Global Detection and Local Segmentation,” IEEE Access, vol. 8, pp. 59934 – 59946, 2020, doi: 10.1109/ACCESS.2020.2982288.
  • [18] Zhao, Z., Qi, H., Qi, Y., Zhang, K., Zhai, Y., and Zhao, W.. “Detection Method Based on Automatic Visual Shape Clustering for Pin-Missing Defect in Transmission Lines,” IEEE Trans Instrum Meas, vol. 69, no. 9, pp. 6080–6091, 2020, doi: 10.1109/TIM.2020.2969057.
  • [19] Wang, S., Liu, Y., Qing, Y., Wang, C., Lan, T., and Yao, R.. “Detection of Insulator Defects With Improved ResNeSt and Region Proposal Network,” IEEE Access, vol. 8, pp. 184841–184850, 2020, doi: 10.1109/ACCESS.2020.3029857.
  • [20] Li, F. et al.. “An automatic detection method of bird’s nest on transmission line tower based on Faster_RCNN,” IEEE Access, vol. 8, pp. 164214 – 164221, 2020, doi: 10.1109/ACCESS.2020.3022419.
  • [21] Han, J. et al.. “A method of insulator faults detection in aerial images for high-voltage transmission lines inspection,” Applied Sciences (Switzerland), vol. 9, no. 10, 2019, doi: 10.3390/app9102009.
  • [22] Liu, C., Wu, Y., Liu, J., and Sun, Z.. “Improved YOLOv3 Network for Insulator Detection in Aerial Images with Diverse Background Interference,” Electronics (Basel), vol. 10, no. 7, 2021, doi: 10.3390/electronics10070771.
  • [23] Liu, J. et al.. “High precision detection algorithm based on improved RetinaNet for defect recognition of transmission lines,” Energy Reports, vol. 6, pp. 2430–2440, 2020, doi: https://doi.org/10.1016/j.egyr.2020.09.002.
  • [24] Miao, X., Liu, X., Chen, J., Zhuang, S., Fan, J., and Jiang, H.. “Insulator Detection in Aerial Images for Transmission Line Inspection Using Single Shot Multibox Detector,” IEEE Access, vol. 7, pp. 9945–9956, 2019, doi: 10.1109/ACCESS.2019.2891123.
  • [25] Wang, L., Chen, Z., Hua, D., and Zheng, Z.. “Semantic Segmentation of Transmission Lines and Their Accessories Based on UAV-Taken Images,” IEEE Access, vol. 7, pp. 80829–80839, 2019, doi: 10.1109/ACCESS.2019.2923024.
  • [26] Zhu, J. et al., “A Deep Learning Method to Detect Foreign Objects for Inspecting Power Transmission Lines,” IEEE Access, vol. 8, pp. 94065–94075, 2020, doi: 10.1109/ACCESS.2020.2995608.
  • [27] Zhao, W., Xu, M., Cheng, X., and Zhao, Z.. “An Insulator in Transmission Lines Recognition and Fault Detection Model Based on Improved Faster RCNN,” IEEE Trans Instrum Meas, vol. 70, pp. 1–8, 2021, doi: 10.1109/TIM.2021.3112227.
  • [28] Redmon, J., Divvala, S., Girshick, R. ve Farhadi, A.. “You Only Look Once: Unified, Real-Time Object Detection,” Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2016, pp. 779–788.
  • [29] Bochkovskiy, A., Wang, C.Y. ve Liao, H.Y.M.. “YOLOv4: Optimal Speed and Accuracy of Object Detection,” arXiv preprint arXiv:2004.10934, 2020.
  • [30] Zhao, Z.-Q., Zheng, P., Xu, S.-T., ve Wu, X.. “Object Detection with Deep Learning: A Review,” IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 11, pp. 3212–3232, 2019.
  • [31] Huang, J., et al.. “Speed/Accuracy Trade-offs for Modern Convolutional Object Detectors,” Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2017, pp. 7310–7311.
  • [32] Ren, S., He, K., Girshick, R. ve Sun, J.. “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” Advances in Neural Information Processing Systems (NeurIPS), 2015, pp. 91–99.
  • [33] Gao, Y., Pan, H., Hu, Y., ve Chen, L.. “Detection of Power Line Insulator Defects Using Deep Convolutional Neural Networks,” IEEE Access, vol. 8, pp. 116071–116080, 2020. [34] Xie, X., Zhang, Y., Qian, Y., ve Wang, Z.. “Insulator Fault Detection in Transmission Lines Using Deep Learning,” Electric Power Systems Research, vol. 167, pp. 39–47, 2019.
  • [35] Lewis, D. and Kulkarni, P.. “Insulator Defect Detection,” IEEE Dataport, 2021, doi: 10.21227/vkdw-x769.
  • [36] Rahman M. A.,Wang, Y.. “Optimizing Intersection-Over-Union in Deep Neural Networks for Image Segmentation,” In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10072. Springer, Cham. https://doi.org/10.1007/978-3-319-50835-1_22

Yıl 2026, Cilt: 16 Sayı: 1, 21 - 31, 31.01.2026

Öz

Kaynakça

  • [1] Han, S., Hao, R., and Lee, J.. “Inspection of Insulators on High-Voltage Power Transmission Lines,” IEEE Transactions on Power Delivery, vol. 24, no. 4, pp. 2319–2327, 2009, doi: 10.1109/TPWRD.2009.2028534.
  • [2] Anjum, S.. “A study of the detection of defects in ceramic insulators based on radio frequency signatures,” University of Waterloo, 2014.
  • [3] Vaillancourt, G. H., Bellerive, J. P., St-Jean, M., and Jean, C.. “New live line tester for porcelain suspension insulators on high-voltage power lines,” IEEE Transactions on Power Delivery, vol. 9, no. 1, pp. 208–219, Jan. 1994, doi: 10.1109/61.277692.
  • [4] Bretuj, W., Fleszynski, J., and Wieczorek, K.. “Diagnostyka izolatorów kompozytowych eksploatowanych w liniach elektroenergetycznych,” Przeglad Elektrotechniczny, vol. 88, no. 5a, pp. 51–54, 2012.
  • [5] Chojnacki, A.. “Analiza niezawodności wybranych urządzeń stacji transformatorowo-rozdzielczych SN/NN,” Energetyka, vol. 7, 2011.
  • [6] Tomaszewski, M., Gasz, R., Kasana, S. S., Osuchowski, J., Singh, S., and Zator, S.. “TCIP: Transformed Colour Intensity Profiles analysis for fault detection in power line insulators,” Multimed Tools Appl, Mar. 2024, doi: 10.1007/s11042-024-18901-w.
  • [7] Wang, X. and Zhang, Y.. “Insulator identification from aerial images using Support Vector Machine with background suppression,” in 2016 International Conference on Unmanned Aircraft Systems (ICUAS), Arlington, VA, USA: IEEE, Jun. 2016, pp. 892–897. doi: 10.1109/ICUAS.2016.7502544.
  • [8] Salustiano, R. et al.. “Development of new methodology for insulators inspections on aerial distribution lines based on partial discharge detection tools,” in 2014 ICHVE International Conference on High Voltage Engineering and Application, Poznan, Poland: IEEE, Sep. 2014, pp. 1–4. doi: 10.1109/ICHVE.2014.7035429.
  • [9] Lee, J.-K., Park, J.-Y., Cho, B.-H., and Oh, K.-Y.. “Development of inspection tool for live-line insulator strings in 154kV power transmission lines,” in 2009 13th European Conference on Power Electronics and Applications, 2009, pp. 1–8.
  • [10] Brito, K. B., Costa, E. G., Dias, B. A., Florentino, M. T. B., and Lira, G. R. S.. “Development of DIP-based algorithm for extraction of UV video attributes from corona discharges on polymeric insulators,” International Journal of Electrical Power & Energy Systems, vol. 134, p. 107406, 2022, doi: https://doi.org/10.1016/j.ijepes.2021.107406.
  • [11] Mirallès, F., Pouliot, N., and Montambault, S.. “State-of-the-art review of computer vision for the management of power transmission lines,” in Proceedings of the 2014 3rd International Conference on Applied Robotics for the Power Industry, 2014, pp. 1–6. doi: 10.1109/CARPI.2014.7030068.
  • [12] Sampedro, C., Rodriguez-Vazquez, J., Rodriguez-Ramos, A., Carrio, A., and Campoy, P.. “Deep Learning-Based System for Automatic Recognition and Diagnosis of Electrical Insulator Strings,” IEEE Access, vol. 7, pp. 101283 – 101308, 2019, doi: 10.1109/ACCESS.2019.2931144.
  • [13] Jiang, H., Qiu, X., Chen, J., Liu, X., Miao, X., and Zhuang, S.. “Insulator Fault Detection in Aerial Images Based on Ensemble Learning with Multi-Level Perception,” IEEE Access, vol. 7, pp. 61797 – 61810, 2019, doi: 10.1109/ACCESS.2019.2915985.
  • [14] Zhang, Y., Huang, X., Jia, J., and Liu, X.. “A Recognition Technology of Transmission Lines Conductor Break and Surface Damage Based on Aerial Image,” IEEE Access, vol. 7, pp. 59022–59036, 2019, doi: 10.1109/ACCESS.2019.2914766.
  • [15] Tao, X., Zhang, D., Wang, Z., Liu, X., Zhang, H., and Xu, D.. “Detection of Power Line Insulator Defects Using Aerial Images Analyzed With Convolutional Neural Networks,” IEEE Trans Syst Man Cybern Syst, vol. 50, no. 4, pp. 1486–1498, 2020, doi: 10.1109/TSMC.2018.2871750.
  • [16] Wen, Q., Luo, Z., Chen, R., Yang, Y., and Li, G.. “Deep Learning Approaches on Defect Detection in High Resolution Aerial Images of Insulators,” Sensors, vol. 21, no. 4, 2021, doi: 10.3390/s21041033.
  • [17] Li, X., Su, H., and Liu, G.. “Insulator Defect Recognition Based on Global Detection and Local Segmentation,” IEEE Access, vol. 8, pp. 59934 – 59946, 2020, doi: 10.1109/ACCESS.2020.2982288.
  • [18] Zhao, Z., Qi, H., Qi, Y., Zhang, K., Zhai, Y., and Zhao, W.. “Detection Method Based on Automatic Visual Shape Clustering for Pin-Missing Defect in Transmission Lines,” IEEE Trans Instrum Meas, vol. 69, no. 9, pp. 6080–6091, 2020, doi: 10.1109/TIM.2020.2969057.
  • [19] Wang, S., Liu, Y., Qing, Y., Wang, C., Lan, T., and Yao, R.. “Detection of Insulator Defects With Improved ResNeSt and Region Proposal Network,” IEEE Access, vol. 8, pp. 184841–184850, 2020, doi: 10.1109/ACCESS.2020.3029857.
  • [20] Li, F. et al.. “An automatic detection method of bird’s nest on transmission line tower based on Faster_RCNN,” IEEE Access, vol. 8, pp. 164214 – 164221, 2020, doi: 10.1109/ACCESS.2020.3022419.
  • [21] Han, J. et al.. “A method of insulator faults detection in aerial images for high-voltage transmission lines inspection,” Applied Sciences (Switzerland), vol. 9, no. 10, 2019, doi: 10.3390/app9102009.
  • [22] Liu, C., Wu, Y., Liu, J., and Sun, Z.. “Improved YOLOv3 Network for Insulator Detection in Aerial Images with Diverse Background Interference,” Electronics (Basel), vol. 10, no. 7, 2021, doi: 10.3390/electronics10070771.
  • [23] Liu, J. et al.. “High precision detection algorithm based on improved RetinaNet for defect recognition of transmission lines,” Energy Reports, vol. 6, pp. 2430–2440, 2020, doi: https://doi.org/10.1016/j.egyr.2020.09.002.
  • [24] Miao, X., Liu, X., Chen, J., Zhuang, S., Fan, J., and Jiang, H.. “Insulator Detection in Aerial Images for Transmission Line Inspection Using Single Shot Multibox Detector,” IEEE Access, vol. 7, pp. 9945–9956, 2019, doi: 10.1109/ACCESS.2019.2891123.
  • [25] Wang, L., Chen, Z., Hua, D., and Zheng, Z.. “Semantic Segmentation of Transmission Lines and Their Accessories Based on UAV-Taken Images,” IEEE Access, vol. 7, pp. 80829–80839, 2019, doi: 10.1109/ACCESS.2019.2923024.
  • [26] Zhu, J. et al., “A Deep Learning Method to Detect Foreign Objects for Inspecting Power Transmission Lines,” IEEE Access, vol. 8, pp. 94065–94075, 2020, doi: 10.1109/ACCESS.2020.2995608.
  • [27] Zhao, W., Xu, M., Cheng, X., and Zhao, Z.. “An Insulator in Transmission Lines Recognition and Fault Detection Model Based on Improved Faster RCNN,” IEEE Trans Instrum Meas, vol. 70, pp. 1–8, 2021, doi: 10.1109/TIM.2021.3112227.
  • [28] Redmon, J., Divvala, S., Girshick, R. ve Farhadi, A.. “You Only Look Once: Unified, Real-Time Object Detection,” Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2016, pp. 779–788.
  • [29] Bochkovskiy, A., Wang, C.Y. ve Liao, H.Y.M.. “YOLOv4: Optimal Speed and Accuracy of Object Detection,” arXiv preprint arXiv:2004.10934, 2020.
  • [30] Zhao, Z.-Q., Zheng, P., Xu, S.-T., ve Wu, X.. “Object Detection with Deep Learning: A Review,” IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 11, pp. 3212–3232, 2019.
  • [31] Huang, J., et al.. “Speed/Accuracy Trade-offs for Modern Convolutional Object Detectors,” Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2017, pp. 7310–7311.
  • [32] Ren, S., He, K., Girshick, R. ve Sun, J.. “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” Advances in Neural Information Processing Systems (NeurIPS), 2015, pp. 91–99.
  • [33] Gao, Y., Pan, H., Hu, Y., ve Chen, L.. “Detection of Power Line Insulator Defects Using Deep Convolutional Neural Networks,” IEEE Access, vol. 8, pp. 116071–116080, 2020. [34] Xie, X., Zhang, Y., Qian, Y., ve Wang, Z.. “Insulator Fault Detection in Transmission Lines Using Deep Learning,” Electric Power Systems Research, vol. 167, pp. 39–47, 2019.
  • [35] Lewis, D. and Kulkarni, P.. “Insulator Defect Detection,” IEEE Dataport, 2021, doi: 10.21227/vkdw-x769.
  • [36] Rahman M. A.,Wang, Y.. “Optimizing Intersection-Over-Union in Deep Neural Networks for Image Segmentation,” In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10072. Springer, Cham. https://doi.org/10.1007/978-3-319-50835-1_22
Toplam 35 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Elektrik Tesisleri, Yüksek Gerilim, Elektrik Mühendisliği (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Zeliha Doğan Ersoy

Muhammed Erdem İsenkul 0000-0003-0856-2174

Gönderilme Tarihi 9 Ekim 2025
Kabul Tarihi 6 Aralık 2025
Yayımlanma Tarihi 31 Ocak 2026
Yayımlandığı Sayı Yıl 2026 Cilt: 16 Sayı: 1

Kaynak Göster

APA Doğan Ersoy, Z., & İsenkul, M. E. (2026). Elektrik İletim ve Dağıtım Hatlarındaki İzolatör Tespiti ve Sınıflandırılması Detection and Classification of Insulators in Electrical Transmission and Distribution Lines. EMO Bilimsel Dergi, 16(1), 21-31. https://izlik.org/JA88PE23XL
AMA 1.Doğan Ersoy Z, İsenkul ME. Elektrik İletim ve Dağıtım Hatlarındaki İzolatör Tespiti ve Sınıflandırılması Detection and Classification of Insulators in Electrical Transmission and Distribution Lines. EMO Bilimsel Dergi. 2026;16(1):21-31. https://izlik.org/JA88PE23XL
Chicago Doğan Ersoy, Zeliha, ve Muhammed Erdem İsenkul. 2026. “Elektrik İletim ve Dağıtım Hatlarındaki İzolatör Tespiti ve Sınıflandırılması Detection and Classification of Insulators in Electrical Transmission and Distribution Lines”. EMO Bilimsel Dergi 16 (1): 21-31. https://izlik.org/JA88PE23XL.
EndNote Doğan Ersoy Z, İsenkul ME (01 Ocak 2026) Elektrik İletim ve Dağıtım Hatlarındaki İzolatör Tespiti ve Sınıflandırılması Detection and Classification of Insulators in Electrical Transmission and Distribution Lines. EMO Bilimsel Dergi 16 1 21–31.
IEEE [1]Z. Doğan Ersoy ve M. E. İsenkul, “Elektrik İletim ve Dağıtım Hatlarındaki İzolatör Tespiti ve Sınıflandırılması Detection and Classification of Insulators in Electrical Transmission and Distribution Lines”, EMO Bilimsel Dergi, c. 16, sy 1, ss. 21–31, Oca. 2026, [çevrimiçi]. Erişim adresi: https://izlik.org/JA88PE23XL
ISNAD Doğan Ersoy, Zeliha - İsenkul, Muhammed Erdem. “Elektrik İletim ve Dağıtım Hatlarındaki İzolatör Tespiti ve Sınıflandırılması Detection and Classification of Insulators in Electrical Transmission and Distribution Lines”. EMO Bilimsel Dergi 16/1 (01 Ocak 2026): 21-31. https://izlik.org/JA88PE23XL.
JAMA 1.Doğan Ersoy Z, İsenkul ME. Elektrik İletim ve Dağıtım Hatlarındaki İzolatör Tespiti ve Sınıflandırılması Detection and Classification of Insulators in Electrical Transmission and Distribution Lines. EMO Bilimsel Dergi. 2026;16:21–31.
MLA Doğan Ersoy, Zeliha, ve Muhammed Erdem İsenkul. “Elektrik İletim ve Dağıtım Hatlarındaki İzolatör Tespiti ve Sınıflandırılması Detection and Classification of Insulators in Electrical Transmission and Distribution Lines”. EMO Bilimsel Dergi, c. 16, sy 1, Ocak 2026, ss. 21-31, https://izlik.org/JA88PE23XL.
Vancouver 1.Doğan Ersoy Z, İsenkul ME. Elektrik İletim ve Dağıtım Hatlarındaki İzolatör Tespiti ve Sınıflandırılması Detection and Classification of Insulators in Electrical Transmission and Distribution Lines. EMO Bilimsel Dergi [Internet]. 01 Ocak 2026;16(1):21-3. Erişim adresi: https://izlik.org/JA88PE23XL

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