Top

Hint

Swipe to navigate through the articles of this issue

Published in:

11-09-2022 | Original Paper

Classification of distribution power grid structures using inception v3 deep neural network

Authors: Stefano Frizzo Stefenon, Kin-Choong Yow, Ademir Nied, Luiz Henrique Meyer

Published in: Electrical Engineering | Issue 6/2022

Abstract

To maintain the supply of electrical energy, it is necessary that failures in the distribution grid are identified during inspections of the electrical power system before shutdowns occur. To automate the inspections, artificial intelligence techniques based on computer vision are proposed. Due to the low number of visible faults, it is difficult to train deep learning models based on images of electrical power system inspections. In this paper, it is proposed to use segmentation and edge detection techniques to increase the database, making classification possible using the Inception v3 deep neural network model. From a pre-processing using the Gaussian filter to smooth the image, the techniques of the threshold with binarization, adaptive binarization, and Otsu and riddler-calvard are used for segmentation; and for edge detection, the sobel and canny techniques are used. The Inception v3 had better results than VGG-16 and ResNet50, considering mean squared error, root mean square error, accuracy, precision, recall, F-measure, and speed to convergence in this application.

previous article Fault detection method of flexible DC distribution network based on color relation analysis classifier

next article A composite network model for load-independent output and near unit power factor in WPT system

To get access to this content you need the following product:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 69.000 Bücher
über 500 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt 90 Tage mit der neuen Mini-Lizenz testen!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 50.000 Bücher
über 380 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt 90 Tage mit der neuen Mini-Lizenz testen!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 58.000 Bücher
über 300 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt 90 Tage mit der neuen Mini-Lizenz testen!

inform now

https://github.com/ElectricalPowerGrid/InspectionDataSet.

Yang L, Fan J, Liu Y, Li E, Peng J, Liang Z (2020) A review on state-of-the-art power line inspection techniques. IEEE Trans Instrum Meas 69(12):9350–9365. https://doi.org/10.1109/TIM.2020.3031194 CrossRef

Reza Ahmadi-Veshki M, Mirzaie M, Sobhani R (2020) Reliability assessment of aged sir insulators under humidity and pollution conditions. Int J Electr Power Energy Syst 117:105679. https://doi.org/10.1016/j.ijepes.2019.105679 CrossRef

Stefenon SF, Ribeiro MHDM, Nied A, Mariani VC, Coelho LDS, Leithardt VRQ, Silva LA, Seman LO (2021) Hybrid wavelet stacking ensemble model for insulators contamination forecasting. IEEE Access 9:66387–66397. https://doi.org/10.1109/ACCESS.2021.3076410 CrossRef

Liu Y, Zong H, Gao S, Du BX (2020) Contamination deposition and discharge characteristics of outdoor insulators in fog-haze conditions. Int J Electr Power Energy Syst 121:106176. https://doi.org/10.1016/j.ijepes.2020.106176 CrossRef

Mussina D, Irmanova A, Jamwal PK, Bagheri M (2020) Multi-modal data fusion using deep neural network for condition monitoring of high voltage insulator. IEEE Access 8:184486–184496. https://doi.org/10.1109/ACCESS.2020.3027825 CrossRef

Medeiros A, Sartori A, Stefenon SF, Meyer LH, Nied A (2021) Comparison of artificial intelligence techniques to failure prediction in contaminated insulators based on leakage current. J Intell Fuzzy Syst 4:3285–3298. https://doi.org/10.3233/JIFS-211126 CrossRef

Qiao X, Zhang Z, Sundararajan R, Jiang X, Hu J, Fang Z (2021) The failure arc paths of the novel device combining an arrester and an insulator under different pollution levels. Int J Electr Power Energy Syst 125:106549. https://doi.org/10.1016/j.ijepes.2020.106549 CrossRef

Rocha PHV, Costa EG, Serres AR, Xavier GVR, Peixoto JEB, Lins RL (2019) Inspection in overhead insulators through the analysis of the irradiated RF spectrum. Int J Electr Power Energy Syst 113:355–361. https://doi.org/10.1016/j.ijepes.2019.05.060 CrossRef

Miao X, Liu X, Chen J, Zhuang S, Fan J, Jiang H (2019) Insulator detection in aerial images for transmission line inspection using single shot multibox detector. IEEE Access 7:9945–9956. https://doi.org/10.1109/ACCESS.2019.2891123 CrossRef

10.

Sampedro C, Rodriguez-Vazquez J, Rodriguez-Ramos A, Carrio A, Campoy P (2019) Deep learning-based system for automatic recognition and diagnosis of electrical insulator strings. IEEE Access 7:101283–101308. https://doi.org/10.1109/ACCESS.2019.2931144 CrossRef

11.

Shi C, Huang Y (2021) Cap-count guided weakly supervised insulator cap missing detection in aerial images. IEEE Sens J 21(1):685–691. https://doi.org/10.1109/JSEN.2020.3012780 CrossRef

12.

Prates RM, Cruz R, Marotta AP, Ramos RP, Simas Filho EF, Cardoso JS (2019) Insulator visual non-conformity detection in overhead power distribution lines using deep learning. Comput Electr Eng 78:343–355. https://doi.org/10.1016/j.compeleceng.2019.08.001 CrossRef

13.

Zhao Z, Zhen Z, Zhang L, Qi Y, Kong Y, Zhang K (2019) Insulator detection method in inspection image based on improved faster r-CNN. Energies 12(7):1204. https://doi.org/10.3390/en12071204 CrossRef

14.

Wu Q, An J (2014) An active contour model based on texture distribution for extracting inhomogeneous insulators from aerial images. IEEE Trans Geosci Remote Sens 52(6):3613–3626. https://doi.org/10.1109/TGRS.2013.2274101 CrossRef

15.

Stefenon SF, Corso MP, Nied A, Perez FL, Yow K-C, Gonzalez GV, Leithardt VRQ (2021) Classification of insulators using neural network based on computer vision. IET Gener, Transm Distrib 16(6):1096–1107. https://doi.org/10.1049/gtd2.12353 CrossRef

16.

Kang G, Gao S, Yu L, Zhang D (2019) Deep architecture for high-speed railway insulator surface defect detection: denoising autoencoder with multitask learning. IEEE Trans Instrum Meas 68(8):2679–2690. https://doi.org/10.1109/TIM.2018.2868490 CrossRef

17.

Ibrahim A, Dalbah A, Abualsaud A, Tariq U, El-Hag A (2020) Application of machine learning to evaluate insulator surface erosion. IEEE Trans Instrum Meas 69(2):314–316. https://doi.org/10.1109/TIM.2019.2956300 CrossRef

18.

Zhang D, Gao S, Yu L, Kang G, Wei X, Zhan D (2021) Defgan: defect detection gans with latent space pitting for high-speed railway insulator. IEEE Trans Instrum Meas 70:1–10. https://doi.org/10.1109/TIM.2020.3038008 CrossRef

19.

Tao X, Zhang D, Wang Z, Liu X, Zhang H, Xu D (2020) Detection of power line insulator defects using aerial images analyzed with convolutional neural networks. IEEE Trans Syst, Man, Cybern: Syst 50(4):1486–1498. https://doi.org/10.1109/TSMC.2018.2871750 CrossRef

20.

Wang S, Liu Y, Qing Y, Wang C, Lan T, Yao R (2020) Detection of insulator defects with improved resnest and region proposal network. IEEE Access 8:184841–184850. https://doi.org/10.1109/ACCESS.2020.3029857 CrossRef

21.

Cui L, Ramesh M (2020) Prediction of flashover voltage using electric field measurement on clean and polluted insulators. Int J Electr Power Energy Syst 116:105574. https://doi.org/10.1016/j.ijepes.2019.105574 CrossRef

22.

Stefenon SF, Singh G, Yow K-C, Cimatti A (2022) Semi-protopnet deep neural network for the classification of defective power grid distribution structures. Sensors 22(13):4859. https://doi.org/10.3390/s22134859 CrossRef

23.

Corso MP, Perez FL, Stefenon SF, Yow K-C, García Ovejero R, Leithardt VRQ (2021) Classification of contaminated insulators using k-nearest neighbors based on computer vision. Computers 10(9):112. https://doi.org/10.3390/computers10090112 CrossRef

24.

Salem AA, Abd-Rahman R, Al-Gailani SA, Kamarudin MS, Ahmad H, Salam Z (2020) The leakage current components as a diagnostic tool to estimate contamination level on high voltage insulators. IEEE Access 8:92514–92528. https://doi.org/10.1109/ACCESS.2020.2993630 CrossRef

25.

Dadashizadeh Samakosh J, Mirzaie M (2019) Investigation and analysis of AC flashover voltage of sir insulators under longitudinal and fan-shaped non-uniform pollutions. Int J Electr Power Energy Syst 108:382–391. https://doi.org/10.1016/j.ijepes.2019.01.028 CrossRef

26.

Mohammadi Savadkoohi E, Mirzaie M, Seyyedbarzegar S, Mohammadi M, Khodsuz M, Ghorbani Pashakolae M, Biazar Ghadikolaei M (2020) Experimental investigation on composite insulators AC flashover performance with fan-shaped non-uniform pollution under electro-thermal stress. Int J Electr Power Energy Syst 121:106142. https://doi.org/10.1016/j.ijepes.2020.106142 CrossRef

27.

Cao B, Wang L, Yin F (2019) A low-cost evaluation and correction method for the soluble salt components of the insulator contamination layer. IEEE Sens J 19(13):5266–5273. https://doi.org/10.1109/JSEN.2019.2902192 CrossRef

28.

Stefenon SF, Seman LO, Sopelsa Neto NF, Meyer LH, Nied A, Yow KC (2022) Echo state network applied for classification of medium voltage insulators. Int J Electr Power Energy Syst 134:107336. https://doi.org/10.1016/j.ijepes.2021.107336 CrossRef

29.

Stefenon SF, Neto CSF, Coelho TS, Nied A, Yamaguchi CK, Yow K-C (2022) Particle swarm optimization for design of insulators of distribution power system based on finite element method. Electr Eng 104:615–622. https://doi.org/10.1007/s00202-021-01332-3 CrossRef

30.

Salem AA, Abd-Rahman R, Rahiman W, Al-Gailani SA, Al-Ameri SM, Ishak MT, Sheikh UU (2021) Pollution flashover under different contamination profiles on high voltage insulator: Numerical and experiment investigation. IEEE Access 9:37800–37812. https://doi.org/10.1109/ACCESS.2021.3063201 CrossRef

31.

Sopelsa Neto NF, Stefenon SF, Meyer LH, Ovejero RG, Leithardt VRQ (2022) Fault prediction based on leakage current in contaminated insulators using enhanced time series forecasting models. Sensors 22(16):6121. https://doi.org/10.3390/s22166121 CrossRef

32.

Sopelsa Neto NF, Stefenon SF, Meyer LH, Bruns R, Nied A, Seman LO, Gonzalez GV, Leithardt VRQ, Yow K-C (2021) A study of multilayer perceptron networks applied to classification of ceramic insulators using ultrasound. Appl Sci 11(4):1592. https://doi.org/10.3390/app11041592 CrossRef

33.

Yuan Z, Ye Q, Wang Y, Guo Z (2021) State recognition of surface discharges by visible images and machine learning. IEEE Trans Instrum Meas 70:1–11. https://doi.org/10.1109/TIM.2020.3031543 CrossRef

34.

Stefenon SF, Bruns R, Sartori A, Meyer LH, Ovejero RG, Leithardt VRQ (2022) Analysis of the ultrasonic signal in polymeric contaminated insulators through ensemble learning methods. IEEE Access 10:33980–33991. https://doi.org/10.1109/ACCESS.2022.3161506 CrossRef

35.

Kim S, Kim D, Jeong S, Ham J-W, Lee J-K, Oh K-Y (2020) Fault diagnosis of power transmission lines using a UAV-mounted smart inspection system. IEEE Access 8:149999–150009. https://doi.org/10.1109/ACCESS.2020.3016213 CrossRef

36.

Guo Z, Ye Q, Wang Y, Han M (2020) Study of the development of negative dc corona discharges on the basis of visible digital images. IEEE Trans Plasma Sci 48(7):2509–2514. https://doi.org/10.1109/TPS.2020.3000921 CrossRef

37.

Dong Y, Zhang Q (2019) A combined deep learning model for the scene classification of high-resolution remote sensing image. IEEE Geosci Remote Sens Lett 16(10):1540–1544. https://doi.org/10.1109/LGRS.2019.2902675 CrossRef

38.

Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), Boston, USA, pp 1–9 . https://doi.org/10.1109/CVPR.2015.7298594

39.

Jahandad Sam S.M., Kamardin K, Amir Sjarif NN, Mohamed N (2019) Offline signature verification using deep learning convolutional neural network (CNN) architectures Googlenet inception-v1 and inception-v3. Procedia Comput Sci 161:475–483. https://doi.org/10.1016/j.procs.2019.11.147 CrossRef

40.

Wang C, Chen D, Hao L, Liu X, Zeng Y, Chen J, Zhang G (2019) Pulmonary image classification based on inception-v3 transfer learning model. IEEE Access 7:146533–146541. https://doi.org/10.1109/ACCESS.2019.2946000 CrossRef

41.

Stefenon SF, Freire RZ, Meyer LH, Corso MP, Sartori A, Nied A, Klaar ACR, Yow K-C (2020) Fault detection in insulators based on ultrasonic signal processing using a hybrid deep learning technique. IET Sci, Meas Technol 14(10):953–961. https://doi.org/10.1049/iet-smt.2020.0083 CrossRef

42.

Dong N, Zhao L, Wu CH, Chang JF (2020) Inception v3 based cervical cell classification combined with artificially extracted features. Appl Soft Comput 93:106311. https://doi.org/10.1016/j.asoc.2020.106311 CrossRef

43.

Chen Q, Sun Q-S, Ann Heng P, Xia D-S (2008) A double-threshold image binarization method based on edge detector. Pattern Recogn 41(4):1254–1267. https://doi.org/10.1016/j.patcog.2007.09.007 CrossRef

44.

Gatos B, Pratikakis I, Perantonis SJ (2004) An adaptive binarization technique for low quality historical documents. In: Marinai S, Dengel AR (eds) Document analysis systems, vol VI, pp 102–113. Springer, Berlin. https://doi.org/10.1007/978-3-540-28640-0_10

45.

DaPonte J, Sadowski T, Broadbridge CC, Day D, Lehman A, Krishna D, Marinella L, Munhutu P, Sawicki M (2007) Comparison of thresholding techniques on nanoparticle images. In: Rahman Z-U, Reichenbach SE, Neifeld MA (eds) Visual information processing XVI, vol 6575, pp 149–158. SPIE, Orlando. https://doi.org/10.1117/12.714998. International Society for Optics and Photonics

46.

Gao W, Zhang X, Yang L, Liu H (2010) An improved sobel edge detection. In: 2010 3rd international conference on computer science and information technology, Chengdu, China, vol 5, pp 67–71 . https://doi.org/10.1109/ICCSIT.2010.5563693

47.

Ding L, Goshtasby A (2001) On the canny edge detector. Pattern Recogn 34(3):721–725. https://doi.org/10.1016/S0031-3203(00)00023-6 CrossRefMATH

48.

Solís-Pérez JE, Gómez-Aguilar JF, Escobar-Jiménez RF, Reyes-Reyes J (2019) Blood vessel detection based on fractional hessian matrix with non-singular Mittag–Leffler Gaussian kernel. Biomed Signal Process Control 54:101584. https://doi.org/10.1016/j.bspc.2019.101584 CrossRef

49.

Blayvas I, Bruckstein A, Kimmel R (2006) Efficient computation of adaptive threshold surfaces for image binarization. Pattern Recogn 39(1):89–101. https://doi.org/10.1016/j.patcog.2005.08.011 CrossRef

50.

Jia F, Shi C, He K, Wang C, Xiao B (2018) Degraded document image binarization using structural symmetry of strokes. Pattern Recogn 74:225–240. https://doi.org/10.1016/j.patcog.2017.09.032 CrossRef

51.

Zhang Y, Gu N, Zhang X, Lin C (2020) Tire x-ray image defects detection based on adaptive thresholding method. In: Parallel architectures, algorithms and programming, vol 1163, pp 118–129. Springer, Singapore . https://doi.org/10.1007/978-981-15-2767-8_11

52.

Farrahi Moghaddam R, Cheriet M (2010) A multi-scale framework for adaptive binarization of degraded document images. Pattern Recogn 43(6):2186–2198. https://doi.org/10.1016/j.patcog.2009.12.024 CrossRefMATH

53.

Suleyman E, Hamdulla A, Tuerxun P, Moydin K (2021) An adaptive threshold algorithm for offline Uyghur handwritten text line segmentation. Wirel Netw 27:3483–3495. https://doi.org/10.1007/s11276-019-02221-1 CrossRef

54.

Liu Z, Yang C, Huang J, Liu S, Zhuo Y, Lu X (2021) Deep learning framework based on integration of s-mask r-CNN and inception-v3 for ultrasound image-aided diagnosis of prostate cancer. Futur Gener Comput Syst 114:358–367. https://doi.org/10.1016/j.future.2020.08.015 CrossRef

55.

Xu Q, Varadarajan S, Chakrabarti C, Karam LJ (2014) A distributed canny edge detector: algorithm and FPGA implementation. IEEE Trans Image Process 23(7):2944–2960. https://doi.org/10.1109/TIP.2014.2311656 MathSciNetCrossRefMATH

56.

Ho Y, Wookey S (2020) The real-world-weight cross-entropy loss function: modeling the costs of mislabeling. IEEE Access 8:4806–4813. https://doi.org/10.1109/ACCESS.2019.2962617 CrossRef

57.

Xiang M, Yu J, Yang Z, Yang Y, Yu H, He H (2020) Probabilistic power flow with topology changes based on deep neural network. Int J Electr Power Energy Syst 117:105650. https://doi.org/10.1016/j.ijepes.2019.105650 CrossRef

58.

Kasburg C, Stefenon SF (2019) Deep learning for photovoltaic generation forecast in active solar trackers. IEEE Lat Am Trans 17(12):2013–2019. https://doi.org/10.1109/TLA.2019.9011546 CrossRef

59.

Huynh NA, Ng WK, Ariyapala K (2018) Learning under concept drift with follow the regularized leader and adaptive decaying proximal. Expert Syst Appl 96:49–63. https://doi.org/10.1016/j.eswa.2017.11.042 CrossRef

60.

Zang H, Cheng L, Ding T, Cheung KW, Wei Z, Sun G (2020) Day-ahead photovoltaic power forecasting approach based on deep convolutional neural networks and meta learning. Int J Electr Power Energy Syst 118:105790. https://doi.org/10.1016/j.ijepes.2019.105790 CrossRef

61.

Wang S, Wang X, Wang S, Wang D (2019) Bi-directional long short-term memory method based on attention mechanism and rolling update for short-term load forecasting. Int J Electr Power Energy Syst 109:470–479. https://doi.org/10.1016/j.ijepes.2019.02.022

62.

Sideratos G, Hatziargyriou ND (2020) A distributed memory RBF-based model for variable generation forecasting. Int J Electr Power Energy Syst 120:106041. https://doi.org/10.1016/j.ijepes.2020.106041 CrossRef

63.

Theckedath D, Sedamkar R (2020) Detecting affect states using vgg16, resnet50 and se-resnet50 networks. SN Comput Sci 1(79):1–7. https://doi.org/10.1007/s42979-020-0114-9 CrossRef

64.

Ren J-H, Liu F (2019) Predicting software defects using self-organizing data mining. IEEE Access 7:122796–122810. https://doi.org/10.1109/ACCESS.2019.2927489 CrossRef

Title: Classification of distribution power grid structures using inception v3 deep neural network
Authors: Stefano Frizzo Stefenon
Kin-Choong Yow
Ademir Nied
Luiz Henrique Meyer
Publication date: 11-09-2022
Publisher: Springer Berlin Heidelberg
Published in: Electrical Engineering / Issue 6/2022
Print ISSN: 0948-7921
Electronic ISSN: 1432-0487
DOI: https://doi.org/10.1007/s00202-022-01641-1

Springer Professional

Hint

Swipe to navigate through the articles of this issue

Abstract

Please log in to get access to this content

To get access to this content you need the following product:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 6/2022

Research on cross-decoupling of multirelay magnetic coupling wireless power transfer system

Three-phase inverters open-circuit faults diagnosis using an enhanced variational mode decomposition, wavelet packet analysis, and scalar indicators

A dynamic and combined maintenance strategy for photovoltaic power plants considering the dependencies and availability of components

Explicit damping specification in PI controller design for desired control performance of wind power conversion system with permanent magnet generator

A real-time LabVIEW-based emulator for various types of electrical loads

A three-winding coupled-inductor nonisolated high step-up interleaved DC–DC converter with low switch stress