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Knowledge reasoning of transmission line component detection using CRITIC and TOPSIS approaches

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Abstract

With the fast advancement of computer technology, the communication industries have used innovative approaches broadly to improve transmission media and facilitate communication. A transmission medium, often known as a line, is a path that allows data to be transmitted between the sender and recipient. Data are transferred using electromagnetic impulses. Between the sender and the destination, there is a wire-based linkage for the transfer of data. The transmission medium's primary function is to transport bits of data over a network. Transmission line technology has advanced quickly to facilitate easier communication between sender and recipient. Transmission lines are upgraded and communication is improved using upgraded cables like optical fiber and coaxial cable. Communication is still absolutely necessary even when there is heavy jamming and poor propagation, especially when there are interruptions brought on by fabricated nuclear disasters. For component detection in transmission media/lines, various techniques are employed. In the study that is being reported, we used two MCDM-based techniques for decision-making: The technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Criteria Importance Through Inter Criteria Correlation (CRITIC). Here, in order to improve our decision-making, we choose seven criteria and eight choices. Each criterion is given a weight using the CRITIC approach, and the TOPSIS technique is used to select the best method out of those that are accessible. The choice with the best value is placed at the top, while the choice with the worst value is placed at the bottom. The results of the study that was provided indicate that the best strategy was chosen among the available options, and this study can be used as a recommendation for future planning and decision-making.

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References

  • Althi TR, Koley E, Ghosh S (2021) Fuzzy Logic based Fault Detection and Classification scheme for Series Faults in Six Phase Transmission Line. In: 2021 7th International Conference on electrical energy systems (ICEES), 2021, pp 479–483

  • Ashok V, Yadav A (2020) A real-time fault detection and classification algorithm for transmission line faults based on MODWT during power swing. Int Trans Electr Energy Syst 30(1):e12164

    Article  Google Scholar 

  • Chen M et al (2021) Environment Perception technologies for power transmission line inspection robots. J Sensors 20:5559231

    Google Scholar 

  • Chen K, Hu J, He J (2018) Detection and classification of transmission line faults based on unsupervised feature learning and convolutional sparse autoencoder. IEEE Trans Smart Grid 9(3):1748–1758

    Google Scholar 

  • Chen S, Liu J, Yang X (2021) A logical reasoning based decision making method for handling qualitative knowledge. Int J Approx Reason 129:49–63. https://doi.org/10.1016/j.ijar.2020.11.003

    Article  MathSciNet  MATH  Google Scholar 

  • Da Silva AP, Lima AC, Souza SM (2012) Fault location on transmission lines using complex-domain neural networks. Int J Electr Power Energy Syst 43(1):720–727

    Article  Google Scholar 

  • Diakoulaki D, Mavrotas G, Papayannakis L (1995) Determining objective weights in multiple criteria problems: the critic method. Comput Oper Res 22(7):763–770

    Article  MATH  Google Scholar 

  • Ferreira VH, Zanghi R, Fortes MZ, Sotelo GG, Silva RB, Souza JC, Guimarães CH, Gomes S Jr (2016) A survey on intelligent system application to fault diagnosis in electric power system transmission lines. Electric Power Syst Res 136:135–153

    Article  Google Scholar 

  • Hang Y, Khan S, Alharbi A, Nazir S (2022) Assessing English teaching linguistic and artificial intelligence for efficient learning using analytical hierarchy process and technique for order of preference by similarity to ideal solution. J Softw Evol Process. https://doi.org/10.1002/smr.2462

    Article  Google Scholar 

  • Islam MM, Sanjeevikumar P, Pedersen JK, Brice CW. Advanced Digital signal processing based transmission line fault detection and classification. In: IECON 2019-45th Annual Conference of the IEEE Industrial Electronics Society 2019 Oct 14 (Vol. 1, pp 4991–4996). IEEE.

  • Jarrahi MA, Samet H, Ghanbari T (2019) Fast Current-only based fault detection method in transmission line. IEEE Syst J 13(2):1725–1736

    Article  Google Scholar 

  • Kalhori MRN, Emami IT, Fallahi F, Tabarzadi M (2022) A data-driven knowledge-based system with reasoning under uncertain evidence for regional long-term hourly load forecasting. Applied Energy 314:118975

    Article  Google Scholar 

  • Karthick S, Lakshmi K (2015) Wide area backup protection scheme for power transmission lines using PMU‖. Int Res J Eng Technol 2(9):273–281

    Google Scholar 

  • Kesharwani S, Singh DK (2014) Simulation of fault detection for protection of transmission line using neural network. Int Res J Eng Technol 3(5):1367–1371

    Google Scholar 

  • Madkour MA, El-Sayed MAH (1993) A knowledge based approach for setting protective relays in transmission networks. Electric Power Syst Res 27(2):107–115. https://doi.org/10.1016/0378-7796(93)90034-C

    Article  Google Scholar 

  • Matin MA (2018) Transmission Media and Propagation Mechanisms. In: Matin MA (ed) Communication Systems for Electrical Engineers. Springer International Publishing, Cham, pp 15–28

    Chapter  Google Scholar 

  • Ma G-m, Li Y-b, Mao N-q, Shi C, Zhang B, Li C-r (2018) A Fiber Bragg grating-based dynamic tension detection system for overhead transmission line galloping. Sensors 18(2):365. https://doi.org/10.3390/s18020365

    Article  Google Scholar 

  • Mukherjee A, Kundu PK, Das A (2021) Classification and localization of transmission line faults using curve fitting technique with Principal component analysis features. Electr Eng 103(6):2929–44

    Article  Google Scholar 

  • Naseem SA, Eslampanah R, Uddin R (2018) Probability estimation for the fault detection and isolation of pmu-based transmission line system of smart grid. In: 2018 5th International Conference on Electrical and Electronic Engineering (ICEEE) 2018 May 3 (pp 284-288). IEEE.

  • Ning L, Ali Y, Ke H, Nazir S, Huanli Z (2020) A hybrid MCDM approach of selecting lightweight cryptographic cipher based on ISO and NIST lightweight cryptography security requirements for internet of health things. IEEE Access 8:220165–220187

    Article  Google Scholar 

  • Prasad CD, Biswal M, Nayak PK (2019) Wavelet operated single index based fault detection scheme for transmission line protection with swarm intelligent support. Energy Syst 12(2):373–392. https://doi.org/10.1007/s12667-019-00373-9

    Article  Google Scholar 

  • Prasad CD, Biswal M, Nayak PK (2020) Swarm assisted positive sequence current component based directional relaying for transmission line protection. In: 2020 IEEE 9th Power India International Conference (PIICON) 2020 Feb 28 (pp 1–6). IEEE

  • Sadeh J, Afradi H (2009) A new and accurate fault location algorithm for combined transmission lines using adaptive network-based fuzzy inference system. Electr Power Syst Res 79(11):1538–1545

    Article  Google Scholar 

  • Sai Kumar MLS, Kumar J, Mahanty RN (2021) Fault Detection during power swing in a TCSC-compensated transmission line based on clark’s transform and teager-kaiser energy operator. Iran J Sci Technol Trans Electr Eng 45(1):47–60

    Article  Google Scholar 

  • Wang B, Wu R, Zheng Z, Zhang W, Guo J (2017) Study on the method of transmission line foreign body detection based on deep learning. In2017 IEEE Conference on Energy Internet and Energy System Integration (EI2) 2017 Nov 26 (pp 1–5). IEEE

  • Zheng X, Jia R, Gong L, Zhang G, Dang J (2021) Component identification and defect detection in transmission lines based on deep learning. J Intell Fuzzy Syst 40(2):3147–3158

    Article  Google Scholar 

  • Zin AA, Saini M, Mustafa MW, Sultan AR (2015) New algorithm for detection and fault classification on parallel transmission line using DWT and BPNN based on Clarke’s transformation. Neurocomputing 168:983–93

    Article  Google Scholar 

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Authors and Affiliations

  1. Henan Jiuyu Enpai Power Technology Co. Ltd, Zhengzhou, China

    Hai Yu, Yang Gao, Lei Yang & Fengqi Liu

  2. Beijing Yuhang Intelligent Technology Co. Ltd, Beijing, China

    Xiaowei Gao & Qiyun Tan

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  1. Hai Yu
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  2. Yang Gao
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  3. Lei Yang
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  4. Fengqi Liu
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  5. Xiaowei Gao
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Correspondence to Yang Gao.

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Yu, H., Gao, Y., Yang, L. et al. Knowledge reasoning of transmission line component detection using CRITIC and TOPSIS approaches. Soft Comput 27, 991–1004 (2023). https://doi.org/10.1007/s00500-022-07540-8

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  • DOI: https://doi.org/10.1007/s00500-022-07540-8

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