Home Evaluation on the method of restoring the complex communication environment in the field based on the complex low pressure platform simulation platform
Article
Licensed
Unlicensed Requires Authentication

Evaluation on the method of restoring the complex communication environment in the field based on the complex low pressure platform simulation platform

  • Jianfeng Jiang and Wenjun Zhu ORCID logo EMAIL logo
Published/Copyright: September 29, 2023

Abstract

In recent years, with the development of smart grids, people have increasingly high requirements for the safety, stability, and reliability of smart grid operation. In actual operation, smart grids require real-time monitoring and control, while for low-voltage power lines, power line carrier communication is required for data transmission. However, in practical applications on site, the low-voltage power line environment is complex, with ground faults, electrical equipment interference, and other situations that can affect communication quality and reliability, leading to a decrease in the effectiveness of real-time monitoring and control. The research in this paper is based on a complex low voltage platform simulation platform to restore the on-site complex substation communication environment. The purpose is to improve the efficiency of automatic meter reading systems by using various communication methods in low-voltage substations, such as wireless public network point-to-point data transmission, optical fiber communication data transmission, power line carrier data transmission, etc. Automatic meter reading systems are an important means of low voltage substation communication. This article experimentally tested the maximum personnel allocation for meter reading using low voltage carrier communication technology, which was 8 people smaller than the maximum personnel allocation for traditional meter reading, and the minimum personnel allocation was 8 people smaller than the traditional meter reading personnel allocation. The electricity accuracy of meter reading using low voltage carrier communication was up to 96 %, while the electricity accuracy of traditional meter reading was 70 %. The maximum accuracy of line loss statistics for low voltage carrier communication technology meter reading was 90 %, while the maximum accuracy of line loss statistics for traditional meter reading was 68 %. The frequency of load anomalies found in low-voltage carrier communication meter readings within a month ranged from 30 to 60 times, while the traditional meter readings concentrated on 10–25 times. From these data, it is clear that communication meter reading in low-voltage station areas has advantages over traditional meter reading.


Corresponding author: Wenjun Zhu, Marketing Service Center, State Grid Shanghai Electric Power Company, 200000, Shanghai, China, E-mail:

  1. Research ethics: Not applicable.

  2. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: The authors states no conflict of interest.

  4. Research funding: None declared.

  5. Data availability: Not applicable.

References

1. Han, K, Wang, Y, Chen, H, Chen, X, Guo, J, Liu, Z, et al.. A survey on vision transformer. IEEE Trans Pattern Anal Mach Intell 2022;45:87–110. https://doi.org/10.1109/tpami.2022.3152247.Search in Google Scholar

2. Shu, Y, Chen, W. Research and application of UHV power transmission in China. High Volt 2018;3:1–13. https://doi.org/10.1049/hve.2018.0003.Search in Google Scholar

3. He, J, Chen, JN, Liu, S, Kortylewski, A, Yang, C, Bai, Y, et al.. Transfg. A transformer architecture for fine-grained recognition. In: Proceedings of the AAAI conference on artificial intelligence; 2022, vol 36:852–60 pp.10.1609/aaai.v36i1.19967Search in Google Scholar

4. Wang, Y, Zhang, X, Yang, T, Sun, J. Anchor detr. Query design for transformer-based detector. In: Proceedings of the AAAI conference on artificial intelligence; 2022, vol 36:2567–75 pp.10.1609/aaai.v36i3.20158Search in Google Scholar

5. Li, R, Wong, P, Wang, K, Yuan, F. Power quality enhancement and engineering application with high permeability distributed photovoltaic access to low-voltage distribution networks in Australia. Protect Control Mod Power Syst 2020;5:1–7. https://doi.org/10.1186/s41601-020-00163-x.Search in Google Scholar

6. Xue, Y, Zhang, XP, Yang, C. AC filterless flexible LCC HVDC with reduced voltage rating of controllable capacitors. IEEE Trans Power Syst 2018;33:5507–18. https://doi.org/10.1109/tpwrs.2018.2800666.Search in Google Scholar

7. Rao, H, Zhou, Y, Zou, C. Design aspects of hybrid HVDC system. CSEE J Power Energy Syst 2020;7:644–53.Search in Google Scholar

8. Srdic, S, Lukic, S. Toward extreme fast charging: challenges and opportunities in directly connecting to medium-voltage line. IEEE Electrification Mag 2019;7:22–31. https://doi.org/10.1109/mele.2018.2889547.Search in Google Scholar

9. Tang, Z, Hill, DJ, Liu, T. Distributed coordinated reactive power control for voltage regulation in distribution networks. IEEE Trans Smart Grid 2020;12:312–23. https://doi.org/10.1109/tsg.2020.3018633.Search in Google Scholar

10. Kong, X, Zhang, X, Zhang, X, Wang, C, Chiang, HD, Li, P. Adaptive dynamic state estimation of distribution network based on interacting multiple model. IEEE Trans Sustain Energy, APR 2022:643–52. https://doi.org/10.1109/tste.2021.3118030.Search in Google Scholar

11. Deng, J, Lam, CS, Wong, MC, Sin, SW, Martins, RP. Instantaneous power quality indices detection under frequency deviated environment. IET Sci Meas Technol 2019;13:1111–21. https://doi.org/10.1049/iet-smt.2018.5123.Search in Google Scholar

12. Chen, G, Lu, Y, Meng, Y, Li, B, Tan, K, Pei, D, et al.. Fuso: fast multi-path loss recovery for data center networks. IEEE/ACM Trans Netw 2018:1–14.10.1109/TNET.2018.2830414Search in Google Scholar

13. Abusukhon, A, Anwar, MN, Mohammad, Z, Alghannam, B. A hybrid network security algorithm based on Diffie Hellman and Text-to-Image Encryption algorithm. J Discrete Math Sci Cryptogr 2019;22:65–81. https://doi.org/10.1080/09720529.2019.1569821.Search in Google Scholar

14. Habibzadeh, H, Dinesh, K, Shishvan, OR, Boggio-Dandry, A, Sharma, G, Soyata, T. A survey of healthcare Internet of Things (HIoT): a clinical perspective. IEEE Internet Things J 2019;7:53–71. https://doi.org/10.1109/jiot.2019.2946359.Search in Google Scholar PubMed PubMed Central

15. Wheelus, C, Zhu, X. IoT network security: threats, risks, and a data-driven defense framework. IoT 2020;1:259–85. https://doi.org/10.3390/iot1020016.Search in Google Scholar

16. Dai, HN, Zheng, Z, Zhang, Y. Blockchain for internet of things: a survey. IEEE Internet Things J 2019;6:8076–94. https://doi.org/10.1109/jiot.2019.2920987.Search in Google Scholar

17. Qiu, T, Chen, N, Li, K, Atiquzzaman, M, Zhao, W. How can heterogeneous internet of things build our future: a survey. IEEE Commun Surv Tutorials 2018;20:2011–27. https://doi.org/10.1109/comst.2018.2803740.Search in Google Scholar

Received: 2023-03-24
Accepted: 2023-08-17
Published Online: 2023-09-29

© 2023 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 26.9.2025 from https://www.degruyterbrill.com/document/doi/10.1515/ijeeps-2023-0098/html
Scroll to top button