Application of distribution network monitoring information automatic verification platform driven by artificial intelligence in improving acceptance testing and power grid operation and maintenance management efficiency
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Ma Jianwei
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Zhou Zhongqiang
Abstract
In the construction, operation and maintenance of modern power grids, the monitoring, acceptance testing, and operation and maintenance of distribution networks have always been key links. With the rapid development of technologies such as smart grids, the Internet of Things and artificial intelligence (AI), how to enhance the efficiency of distribution network acceptance testing and operation and maintenance management has become an urgent issue to be addressed. However, traditional distribution network acceptance testing and operation and maintenance management rely on manual operations, which are inefficient, error-prone, and lack data integration and intelligent support. In response to the above problems, this paper studies and designs an automatic verification platform for distribution network monitoring information based on AI. The platform first collects and integrates distribution network information, performs preliminary data processing, and then realizes automatic verification and anomaly detection of the monitored platform data through the long short-term memory (LSTM) network model. In addition, the particle swarm optimization (PSO) algorithm is utilized to optimize the task scheduling of the platform, and the greedy algorithm is used to optimize the platform’s load balancing, aiming to enhance the platform’s processing efficiency, response speed, and resource allocation capability. After acceptance testing, the outcomes demonstrate that the platform designed in this paper has good verification consistency for the distribution network, and the verification consistency of various major equipment remains above 90 %. It can detect abnormalities and give feedback for different monitoring points in different distribution network areas. The traditional operation and maintenance solution is compared with the platform designed in this paper regarding operation and maintenance management efficiency. The results show that in the seven key indicators of operation and maintenance management efficiency, the platform designed in this paper has shown obvious advantages, providing a practical solution for the intelligent management of the distribution network and contributing to the development of automation, intelligence, and efficiency of power grid operation and maintenance.
Funding source: Guizhou Power Grid Science and Technology Project, research on automatic verification platform for monitoring information of new distribution network based on Beidou positioning image push technology
Award Identifier / Grant number: GZKJXM20232355
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Research ethics: The local Institutional Review Board deemed the study exempt from review.
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Informed consent: Informed consent was obtained from all individuals included in this study.
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Author contributions: All Author contributed to the design and methodology of this study, the assessment of the outcomes, and the writing of the manuscript.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors declare that they have no conflicts of interest regarding this work.
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Research funding: Guizhou Power Grid Science and Technology Project, research on automatic verification platform for monitoring information of new distribution network based on Beidou positioning image push technology (GZKJXM20232355).
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Data availability: All data generated or analyzed during this study are included in the manuscript.
References
1. Cao, Y, Zhao, Y, Wang, Q, Zhang, J, Ng, SX, Hanzo, L. The evolution of quantum key distribution networks: on the road to the internet. IEEE Commun Surv Tutor 2022;24:839–94. https://doi.org/10.1109/COMST.2022.3144219.Search in Google Scholar
2. Olea-Oregi, E, Eguía-López, P, Sanchez-Ruiz, A, Loureiro-González, I. Industrial overview of back-to-back VSC power links in MV distribution networks. IEEE Trans Smart Grid 2022;14:126–41. https://doi.org/10.1109/TSG.2022.3187157.Search in Google Scholar
3. Peng, J, Kimmig, A, Wang, D, Niu, Z, Fan, Z, Wang, J. A systematic review of data-driven approaches to fault diagnosis and early warning. J Intell Manuf 2023;34:3277–304. https://doi.org/10.1007/s10845-022-02000-0.Search in Google Scholar
4. Li, J, Liu, J, Chen, Y. Based on GRU-CNN, a fault warning for the synchronous generator’s inter-turn short circuit of the excitation winding. Glob Energy Interconnect 2022;5:236–48. https://doi.org/10.1016/j.gloei.2022.06.007.Search in Google Scholar
5. Sakai, RT, Almeida, CFM, Rosa, LHL, Kagan, N, Pereira, DDS, Medeiros, TS. Architecture deployment for application of advanced distribution automation functionalities in smart grids. J Control Autom Electr Syst 2022;33:219–28. https://doi.org/10.1007/s40313-021-00812-5.Search in Google Scholar
6. Shobole, AA. Multi-agent systems-based adaptive protection for the smart distribution network. Elec Power Compon Syst 2022;49:1432–44. https://doi.org/10.1080/15325008.2022.2029490.Search in Google Scholar
7. Han, Y, Yang, D, Zhang, J, Min, B, Liang, Z. Using AI technology to optimize distribution networks. J Electr Syst 2024;20:1259–64.Search in Google Scholar
8. Beeravelly, SR. Engineering the future of green energy: AI-powered integration for optimal resource management. Int J Res Comput Appl Inf Technol (IJRCAIT) 2024;7:1638–49.Search in Google Scholar
9. Arsenault, R, Martel, J-L, Brunet, F, Brissette, F, Mai, J. Continuous streamflow prediction in ungauged basins: long short-term memory neural networks outperform traditional hydrological models. Hydrol Earth Syst Sci 2023;27:139–57. https://doi.org/10.5194/hess-27-139-2023.Search in Google Scholar
10. Wu, J, Wang, Z, Hu, Y, Tao, S, Dong, J. Runoff forecasting using convolutional neural networks and optimized bi-directional long short-term memory. Water Resour Manag 2023;37:937–53. https://doi.org/10.1007/s11269-022-03360-3.Search in Google Scholar
11. Gad, AG. Particle swarm optimization algorithm and its applications: a systematic review. Arch Comput Methods Eng 2022;29:2531–61. https://doi.org/10.1007/s11831-021-09645-1.Search in Google Scholar
12. Lu, C, Zheng, J, Yin, L, Wang, R. An improved iterated greedy algorithm for the distributed hybrid flow shop scheduling problem. Eng Optim 2024;56:792–810. https://doi.org/10.1080/0305215X.2022.2091983.Search in Google Scholar
13. Bozorg, A, Staszewski, RB. A charge-sharing IIR filter with linear interpolation and high stopband rejection. IEEE J Solid State Circ 2022;57:2090–101. https://doi.org/10.1109/JSSC.2022.3170405.Search in Google Scholar
14. Wang, Y, Xiao, Z, Cao, G. A convolutional neural network method based on Adam optimizer with power-exponential learning rate for bearing fault diagnosis. J Vibroeng 2022;24:666–78. https://doi.org/10.21595/jve.2022.22464.Search in Google Scholar
15. Wang, L. Low-latency, high-throughput load balancing algorithms. J Comput Technol Appl Math 2024;1:1–9.Search in Google Scholar
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