Abstract
Infrared (IR) imaging technology has been widely used in various applications mainly in temperature analysis as many objects emit electromagnetic radiation, especially in the electrical equipment that flows with strong current. The equipment could simply lead to overheating and may cause electrical equipment and devices to malfunction. Recently, infrared thermography (IRT) has appeared as an important tool in preventive maintenance for power distribution monitoring and detect defections in electrical equipment based on absolute and relative temperature data as a non-contact temperature distribution measuring technique. However, the conventional method is inefficient and inaccurate since requiring tedious work to manually record the temperature of thermal images. This study is proposed to automatize the power faults detection method using a deep learning method of convolution neural network (CNN) based on thermal imaging. The study involves used of data acquisition, pre-processing to resize and color pre-processing to make it compatible with CNN. Technically, data augmentation is used to double the data in the study and the proposed CNN model frameworks is designed for extracting features. The results of an accuracy score under normal and fault conditions have shown better method’s average accuracy of 83.3% for normal conditions and 82.8% for fault conditions. This study suggested that the detection method using CNN based on thermal imaging has ability to identify between normal and defect condition of power distribution equipment.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Han J, Miao S, Li Y, Yang W, Yin H (2021) Fault diagnosis of power systems using visualized similarity images and improved convolution neural networks. IEEE Syst J. https://doi.org/10.1109/JSYST.2021.3056536
Haider M, Doegar A, Verma RK (2018) Fault identification in electrical equipment using thermal image processing. In: 2018 International conference on computing, power and communication technologies (GUCON), pp 853–858. https://doi.org/10.1109/GUCON.2018.8675
Hurrah NN, Loan NA, Parah SA, Sheikh JA, Muhammad K, de Macedo ARL, de Albuquerque VHC (2021) INDFORG: industrial forgery detection using automatic rotation angle detection and correction. IEEE Trans Industr Inform 17:3630–3639
Parah SA, Sheikh JA, Ahad F, Bhat GM (2017) High capacity and secure electronic patient record (EPR) embedding in color images for IoT driven healthcare systems. In: Internet of things and big data analysis toward next-generation intelligence. Springer Link, pp 409–437
Mansour RF, Parah SA (2021) Reversible data hiding for electronic patient information security for telemedicine applications. Arab J Sci Eng 46:9129–9144
Olivatti Y, Penteado C, Aquino PT, Maia RF (2018) Analysis of artificial intelligence techniques applied to thermographic inspection for automatic detection of electrical problems. In: 2018 IEEE international smart cities conference (ISC2). https://doi.org/10.1109/ISC2.2018.8656724
Maduako I et al (2021) Deep learning for component fault detection in electricity transmission lines. https://doi.org/10.21203/rs.3.rs-1028973/v1
Caetano DG et al (2018) Design and implementation of an automatic vehicle for thermographic inspections in. In: 3rd International conference on computational intelligence and applications, ICCIA 2018, pp 145–150. https://doi.org/10.1109/ICCIA.2018.00034
Woolery B (2021) Infrared thermal imaging-based thermal diffusivity measurement using angstrom method. University of Oklahoma
Shao H et al (2020) Intelligent fault diagnosis of rotor-bearing system under varying working conditions with modified transfer convolutional neural network and thermal images. IEEE Trans Ind 17:3488–3496
Liu CH, Qi Y, Ding WR (2017) Infrared and visible image fusion method based on saliency detection in sparse domain. Infrared Phys Technol 83:94–102
Li H, Li X, Yu Z, Mao C (2016) Multifocus image fusion by combining with mixed-order structure tensors and multiscale neighborhood. Inf Sci 349–350:25–49
Naik VV, Gharge S (2016) Satellite image resolution enhancement using DTCWT and DTCWT based fusion. In: Proceedings of the international conference on advances in computing, Jaipur, pp 1957–1962
Palimkar NH (2016) Fault prediction in electrical equipment using thermographic inspection. Int J Eng Res Technol 5:685–687
Hurrah NN, Parah SA, Sheikh JA (2020) Embedding in medical images: an efficient scheme for authentication and tamper localization. Multimed Tools Appl 79:21441–21470
Bhat GM, Mustafa M, Ahmad S, Ahmad J (2009) VHDL modeling and simulation of data scrambler and descrambler for secure data communication. Indian J Sci Technol 2(10):41–44
Kazim M, Khawaja AH, Zabit U, Huang Q (2020) Fault detection and localization for overhead 11-kV distribution lines with magnetic measurements. IEEE Trans Instrum Meas 69(5):2028–2038. https://doi.org/10.1109/TIM.2019.2920184
Zhao M, Barati M (2021) A real-time fault localization in power distribution grid for wildfire detection through deep convolutional neural networks. IEEE Trans Ind Appl 57(4):4316–4326. https://doi.org/10.1109/TIA.2021.3083645
Alashter MA, Mrehel OG, Shamekh AS (2020) Design and evaluation a distance relay model based on artificial neural networks (ANN). In: 6th IEEE international energy conference (ENERGYCON). IEEE, Piscataway
Sun K, Chen Q, Gao Z (2016) An automatic faulted line section location method for electric power distribution systems based on multisource information. IEEE Trans Power Deliv 31(4):1542–1551. https://doi.org/10.1109/TPWRD.2015.2473681
Zeng F, Zhang Q, Wang J, Chen Y, Qi G, Zhu Z (2018) Morphology-based visible-infrared image fusion framework for smart city. Int J Simul Process Model 13(6):523. https://doi.org/10.1504/IJSPM.2018.10016917
Aziz F, Ul Haq A, Ahmad S, Mahmoud Y, Jalal M, Ali U (2020) A novel convolutional neural network-based approach for fault classification in photovoltaic arrays. IEEE Access 8:41889–41904. https://doi.org/10.1109/ACCESS.2020.2977116
Fahim SR, Sarker SK, Muyeen SM, Das SK, Kamwa I (2021) A deep learning based intelligent approach in detection and classification of transmission line faults. Int J Electr Power Energy Syst 133. https://doi.org/10.1016/j.ijepes.2021.107102
Ren H, Hou ZJ, Vyakaranam B, Wang H, Etingov P (2020) Power system event classification and localization using a convolutional neural network. Front Energy Res 8. https://doi.org/10.3389/fenrg.2020.607826
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
Gu H, Wang Y, Hong S, Gui G (2019) Blind channel identification aided generalized automatic modulation recognition based on deep learning. IEEE Access 7:110722–110729
Mukherjee A, Kundu PK, Das A (2021) Transmission line faults in power system and the different algorithms for identification, classification and localization: a brief review of methods. J Inst Eng India Ser B 102:855–877. https://doi.org/10.1007/s40031-020-00530-0
Siriwithtayathanakun P, Sriyanyong P (2018) Effect of faults on electrical equipment in power substation: a case study of metropolitan electricity authority’s power system. In: 2018 15th International conference on electrical engineering/electronics, computer, telecommunications and information technology (ECTI-CON), pp 176–179. https://doi.org/10.1109/ECTICon.2018.8619878
Wei H et al (2019) A novel precise decomposition method for infrared and visible image fusion. In: 2019 Chinese Control Conference (CCC), pp 3341–3345. https://doi.org/10.23919/ChiCC.2019.8865921
Gowrishankar M, Nagaveni P, Balakrishnan P (2016) Transmission line fault detection and classification using discrete wavelet transform and artificial neural network. Middle-East J Sci Res 24:1112–1121
Sowah RA et al (2018) Design of power distribution network fault data collector for fault detection, location and classification using machine learning. In: 2018 IEEE 7th international conference on adaptive science & technology (ICAST), pp 1–8. https://doi.org/10.1109/ICASTECH.2018.8506774
Huang X, Shang E, Xue J, Ding H, Li P (2020) A multi-feature fusion-based deep learning for insulator image identification and fault detection. In: 2020 IEEE 4th information technology, networking, electronic and automation control conference (ITNEC), pp 1957–1960. https://doi.org/10.1109/ITNEC48623.2020.9085037
TICOR (2012) Thermal imaging report. Thermal Imaging Ltd
Prabhu (2018) Understanding of convolutional neural network (CNN) – deep learning. 4 Mar 2018
Zahisham Z, Lee CP, Lim KM (2020) Food recognition with ResNet-50. In: 2020 IEEE 2nd international conference on artificial intelligence in engineering and technology (IICAIET), pp 1–5. https://doi.org/10.1109/IICAIET49801.2020.9257825
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Ishak, N.H., Halim, M.A.I., Isa, I.S. (2023). Detection of Power Distribution Fault in Thermal Images Using CNN. In: Parah, S.A., Hurrah, N.N., Khan, E. (eds) Intelligent Multimedia Signal Processing for Smart Ecosystems. Springer, Cham. https://doi.org/10.1007/978-3-031-34873-0_11
Download citation
DOI: https://doi.org/10.1007/978-3-031-34873-0_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-34872-3
Online ISBN: 978-3-031-34873-0
eBook Packages: Computer ScienceComputer Science (R0)