Abstract
To address the challenges of acquiring fault data and the time-intensive nature of maintenance task distribution in power communication networks, this paper presents an optimization of preparation work for Communication operation and maintenance system based on intelligent auxiliary module. When contrasted with conventional manual techniques, the system enhances the operation and maintenance planning through intelligent processing. It unifies three sub-modules - maintenance management, ticket information, and approval of maintenance - achieving precise amalgamation of maintenance data, smart assistance for operation and maintenance preparation, and historical iterative optimization of such information. The pilot implementation in Shanxi Province demonstrates that the system, through its intelligent support for onsite operation and maintenance, substantially diminishes average maintenance duration and heightens the efficiency of power communication network operation and maintenance.
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References
Ning, F.: Research on a kind of ubiquitous power internet of things system for strong smart power grid. J. Electr. Eng. Technol. 16(3), 1387–1396 (2021)
Colak, I.: Analyzing the efficient use of energy in a small smart grid system. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, pp. 1–6 (2019)
Peng, C.N.: Design and analysis of intelligent operation and maintenance platform system. In: 2017 36th Chinese Control Conference, pp. 10377–10381 (2017)
Li, Q., Yuan, F., Chen, J., Shi, S., Xu, S.: The automated operation and maintenance solution for cloud data centers based on multi-station integration. In: Tavana, M., Nedjah, N., Alhajj, R. (eds.) IISA 2020. AISC, vol. 1304, pp. 450–458. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-63784-2_56
Li, X.: Research and application of smart grid early warning decision platform based on big data analysis. In: 2020 IEEE International Conference on Power System Technology, pp. 1–6. (2021)
Fan, Y., et al.: Design of integrated operation and maintenance platform based on AIOps. In: Kountchev, R., Patnaik, S., Shi, J., Favorskaya, M.N. (eds.) Advances in 3D Image and Graphics Representation, Analysis, Computing and Information Technology. SIST, vol. 180, pp. 437–442. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-3867-4_51
Liang, Y.: Discussion about integrated operation and maintenance framework for information and communication network of power grid. In: 2014 International Conference on Industrial Engineering and Information Technology, pp. 1052–1055 (2014)
Wang, X.: Research on intelligent operation and maintenance management method of enterprise IT. J. Phys.: Conf. Ser. 1869(2) (2021)
Ren, S.: A personalized operation and maintenance approach for complex products based on equipment portrait of product-service system. Robot. Comput.-Integr. Manuf. 80, 102485 (2023)
Zhang, Y.: Design-manufacturing-operation & maintenance (O&M) integration of complex products based on digital twin. Appl. Sci. 13(2), 1052 (2013)
Acknowledgments
This paper is supported by the 2022 Mass Innovation Project of Shanxi Electric Power Company Information Communication Branch Company (SGSXXT00JFJS2200271).
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Gao, Y. et al. (2024). Optimization of Preparation Work for Communication Operation and Maintenance System Based on Intelligent Auxiliary Module. In: Hu, C., Cao, W. (eds) Conference Proceedings of the 2023 3rd International Joint Conference on Energy, Electrical and Power Engineering. CoEEPE 2023. Lecture Notes in Electrical Engineering, vol 1208. Springer, Singapore. https://doi.org/10.1007/978-981-97-3940-0_71
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DOI: https://doi.org/10.1007/978-981-97-3940-0_71
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