Neural network-based fiber optic cable fault prediction study for power distribution communication network

[1] Wang, H., Huang, H., Chen, A., Li, X., & Li, F. (2021). Real-time fault diagnosis method for low-voltage power line carrier communication network based on network topology. International journal of internet protocol technology(3), 14. Search in Google Scholar

[2] A, Z. L., A, X. L., A, Y. Y., A, Y. Y., & B, X. G. A. (2021). Game based robust power allocation strategy with qos guarantee in d2d communication network. Computer Networks. Search in Google Scholar

[3] Hudson, S. M., Taylor, J. T., & Bowen, C. R. (2022). Energy harvesting of cathodic protection currents in subsea and marine structures for wireless sensor power and communication. Applied energy(Jun.15), 316. Search in Google Scholar

[4] Yang, D., Ji, M., Lv, Y., Zhao, F., Geng, Q., & Bhola, J. (2022). Research on zoning, optimization, stability, and nonlinear control of wireless network in power grid communication. Journal of Interconnection Networks, 22(Supp06). Search in Google Scholar

[5] Yang, L., Hu, Z., Hao, Y., Qiu, W., & Li, L. (2021). Internal temperature measurement and conductor temperature calculation of xlpe power cable based on optical fiber at different radial positions. Engineering Failure Analysis. Search in Google Scholar

[6] Xie, Y., & Wang, C. (2021). Vulnerability of submarine cable network of mainland china: comparison of vulnerability between before and after construction of trans-arctic cable system. Complexity, 2021. Search in Google Scholar

[7] Yang, Qing, Tao, Jiayuan, He, & Yanxiao, et al. (2018). Analysis and suppression measures of lightning transient overvoltage in the signal cable of wind turbines. Wind Energy. Search in Google Scholar

[8] Yang, Q., Tao, J., He, Y., & Zhang, B. (2017). Analysis and suppression measures of lightning transient overvoltage in the signal cable of wind turbines. Wind Energy, 21(4), 211-225. Search in Google Scholar

[9] Beerten Jef, D’Arco Salvatore, & Suul Jon Are. (2016). Frequency-dependent cable modelling for small-signal stability analysis of vsc-hvdc systems. IET Generation, Transmission & Distribution. Search in Google Scholar

[10] Zhang, Z. F., Chen, Y. H., De-Jun, L. I., Jin, B., Yang, C. J., & Wang, J. (2018). Use of a coded voltage signal for cable switching and fault isolation in cabled seafloor observatories. Frontiers of Information Technology & Electronic Engineering. Search in Google Scholar

[11] Marriott, N. (2021). Megger launches cable fault locator. Electrical engineering(Jun.). Search in Google Scholar

[12] Baranowski, J., Grobler-Dbska, K., & Kucharska, E. (2021). Recognizing vsc dc cable fault types using bayesian functional data depth. Energies, 14. Search in Google Scholar

[13] Liu, Y., Xiong, H., & Xiao, H. (2022). Detecting xlpe cable insulation damage based on distributed optical fiber temperature sensing. Optical Fiber Technology, 68, 102806-. Search in Google Scholar

[14] Chen, Y., Li, X., Cai, C., Wu, C., Zhang, W., & Huang, X., et al. (2021). Submarine cable detection method based on multisensor communication. Journal of Sensors(Pt.11), 2021. Search in Google Scholar

[15] Lee, C. K., Lee, G. S., & Chang, S. J. (2021). Solution to fault of multi-terminal dc transmission systems based on high temperature superconducting dc cables. Energies, 14(5), 1292. Search in Google Scholar

[16] Lai, Q., Chen, J., Hu, L., Cao, J., & Zhu, N. (2020). Investigation of tail pipe breakdown incident for 110 kv cable termination and proposal of fault prevention. Engineering Failure Analysis, 108, 104353. Search in Google Scholar

[17] Jeon, J. C., Kim, T. H., & Yoo, J. G. (2015). Fault detection of low voltage cable using time-frequency correlation in sstdr. Transactions of the Korean Institute of Electrical Engineers, 64(3), 498-504. Search in Google Scholar

[18] Zhu, Q. X., Jin, C., He, Y. L., & Xu, Y. (2021). Pattern mining of alarm flood sequences using an improved prefixspan algorithm with tolerance to short-term order ambiguity. Industrial & Engineering Chemistry Research. Search in Google Scholar

[19] Niyazmand, T., & Izadi, I. (2019). Pattern mining in alarm flood sequences using a modified prefixspan algorithm. ISA Transactions, 90. Search in Google Scholar

[20] Cheng, L., Tong, Z., Xie, S., & Kersemans, M. (2022). Irt-gan: a generative adversarial network with a multi-headed fusion strategy for automated defect detection in composites using infrared thermography. Composite structures(Jun.), 290. Search in Google Scholar

[21] Kwon, H., Huh, H., Seo, H., Han, S., Won, I., & Sue, J., et al. (2022). Tcad augmented generative adversarial network for hot-spot detection and mask-layout optimization in a large area harc etching process. Physics of plasmas(7), 29. Search in Google Scholar

[22] Xu, F., Dong, G., Li, J., Yang, Q., Wang, L., & Zhao, Y., et al. (2022). Deep convolution generative adversarial network-based electroencephalogram data augmentation for post-stroke rehabilitation with motor imagery. International journal of neural systems, 32(9), 2250039. Search in Google Scholar