Abstract
Globally, 80% of the world population use electricity as a prime energy source. Government and private organizations face many challenges in providing efficient power facilities to their customers due to over-population and exponential increase in electricity demands. Furthermore, the abrupt damages in transmission lines pose another big barrier in the form of reliable and safer power transmissions. These line damages become more severe when the transmission infrastructure spans thousands of kilometers. Mostly, it results in life loss (humans and cattle), destruction of homes and crops, over-costing, etc. To address these problems, a hybrid deep learning mechanism is proposed in this research work that can accurately identify the damages in the transmission lines. This model consists of convolution neural network (CNN) and support vector machine (SVM) where CNN is used for the classification damaged power-line images, while SVM for the identification and calculating the severity of damaged power-lines using statistical information. Applicability of the model is validated using UAVs and other performance metrics such as accuracy, precision, F-score, error-rate, simulation time, area under curve values, and True–False values. The proposed model outperformed by generating a high recognition rate of 95.57% for the identification of damaged power-lines. The implications of this research work include no humans and cattle life loss, no extra transmission lines management and checkup costs, no destruction of homes crops, etc.
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References
Ahmed I, Jeon G, Piccialli F (2021) A deep learning-based smart healthcare system for patient’s discomfort detection at the edge of internet of things. IEEE Internet Things J
Bruch M, Munch V, Aichinger M, Kuhn M, Weymann M, Schmid G (2011) Power blackout risks. Risk management options. Emerging risk initiative—position paper. CRO Forum. November, 2011
Castillo A (2014) Risk analysis and management in power outage and restoration: a literature survey. Electric Power Syst Res 107:9–15. 02/01/ 2014
Deveci M, Pamucar D, Gokasar I (2021) Fuzzy power heronian function based CoCoSo method for the advantage prioritization of autonomous vehicles in real-time traffic management. Sustain Cities Soc 69:102846
Dodiya M, Shah M (2021) A systematic study on shaping the future of solar prosumage using deep learning. Int J Energy Water Resources. 03/13/2021.
Dong S (2021) Multi class SVM algorithm with active learning for network traffic classification. Exp Syst Appl 176:114885
Duan M, Li K, Yang C, Li K (2018) A hybrid deep learning CNN–ELM for age and gender classification. Neurocomputing 275:448–461
Hasan M, Toma RN, Nahid A-A, Islam M, Kim J-M (2019) Electricity theft detection in smart grid systems: a CNN-LSTM based approach. Energies 12:3310
Hu T, Guo Q, Shen X, Sun H, Wu R, Xi H (2019) Utilizing unlabeled data to detect electricity fraud in AMI: a semisupervised deep learning approach. IEEE Trans Neural Netw Learn Syst 30:3287–3299
Jeyaraj PR, Nadar ERS, Kathiresan AC, Asokan SP (2020) Smart grid security enhancement by detection and classification of non‐technical losses employing deep learning algorithm. Int Trans Electric Energy Syst 30:e12521
Khan ZA, Adil M, Javaid N, Saqib MN, Shafiq M, Choi J-G (2020) Electricity theft detection using supervised learning techniques on smart meter data. Sustainability 12:8023
Khan S, Hafeez A, Ali H, Nazir S, Hussain A (2020) Pioneer dataset and recognition of handwritten pashto characters using convolution neural networks. In: Measurement and Control, 0020294020964826
Kong X, Zhao X, Liu C, Li Q, Dong D, Li Y (2021) Electricity theft detection in low-voltage stations based on similarity measure and DT-KSVM. Int J Electric Power Energy Syst 125: 106544. 02/01/ 2021
Lalitha VL, Raju SH, Sonti VK, Mohan VM (2021) customized smart object detection: statistics of detected objects using IoT. Int Conf Artif Intell Smart Syst ICAIS 2021:1397–1405
Li J, Cheng K, Wang S, Morstatter F, Trevino RP, Tang J et al (2017) Feature selection: a data perspective. ACM Comput Surv CSUR 50:1–45
Miller EK (1999) CHAPTER 1—Introduction, in Time domain electromagnetics. Rao SM (ed). Academic Press, San Diego, pp 1–48
Nabil M, Ismail M, Mahmoud M, Shahin M, Qaraqe K, Serpedin E(2019) Deep learning-based detection of electricity theft cyber-attacks in smart grid AMI networks. Deep learning applications for cyber security. Alazab M,Tang M (Eds). Springer International Publishing, Cham., pp 73–102
Nachtigall P, Sauer J (2007) Chapter 20—applications of quantum chemical methods in zeolite science. Studies in surface science and catalysis, vol 168, Čejka J, van Bekkum H, Corma A, Schüth F, (eds). Elsevier, pp 701-XXI.
Nguyen VN, Jenssen R, Roverso D (2018) Automatic autonomous vision-based power-line inspection: a review of current status and the potential role of deep learning. Int J Electric Power Energy Syst 99:107–120, 07/01/2018
Pradeep Y, Khaparde SA, Joshi RK (2011) High level event ontology for multiarea power system. IEEE Trans Smart Grid 3:193–202
Sengan S, Subramaniyaswamy V, Indragandhi V, Velayutham P, Ravi L (2021) Detection of false data cyber-attacks for the assessment of security in smart grid using deep learning. Comput Electric Eng 93:107211. 07/01/2021
Tubishat M, Ja'afar S, Alswaitti M, Mirjalili S, Idris N, Ismail MA et al (2021) Dynamic salp swarm algorithm for feature selection. Expert Syst Appl 164:113873
Wu Y, Tan H, Qin L, Ran B, Jiang Z (2018) A hybrid deep learning based traffic flow prediction method and its understanding. Transp Res Part c Emerg Technolo 90:166–180
Yang H, Zeng R, Xu G, Zhang L (2021) A network security situation assessment method based on adversarial deep learning. Appl Soft Comput 102:107096
Yan K, Zhao J, Ren Y (2021) Electricity theft identification algorithm based on auto-encoder neural network and random forest IEEE 5th advanced information technology. Electron Auto Control Conf IAEAC 2021:2641–2645
Zaman R, Brudermann T (2018) Energy governance in the context of energy service security: a qualitative assessment of the electricity system in Bangladesh. Appl Energy 223:443–456
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This work was supported by the Beijing Yuhang Intelligent Technology Co.Ltd, Beijing 100085, China.
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This work is supported by Beijing Imperial Image Intelligent Technology, Beijing 100085, China.
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Tian, Y., Wang, Q., Guo, Z. et al. A hybrid deep learning and ensemble learning mechanism for damaged power line detection in smart grids. Soft Comput 26, 10553–10561 (2022). https://doi.org/10.1007/s00500-021-06482-x
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DOI: https://doi.org/10.1007/s00500-021-06482-x