Abstract
With the rapid development of industrial manufacturing, the operation and maintenance problems of the power system are becoming increasingly prominent, and traditional manual maintenance methods are no longer able to meet the needs. This article aims to provide better power support for manufacturing enterprises. This article establishes a digital twin model of the power system, which corresponds the physical information and operational data of the actual power system to the digital model. Based on this model for simulation and prediction, machine learning and artificial intelligence algorithms are used to analyze historical data, extract features and patterns, and establish a monitoring and prediction model for the operation status of the power system. Finally, through real-time monitoring and prediction, timely detection and handling of faults and anomalies in the power system, the results can effectively improve the operational efficiency and reliability of the power system in manufacturing enterprises. Through real-time monitoring and prediction, faults and anomalies in the power system can be detected in a timely manner, reducing downtime and maintenance costs. Digital twin technology can also provide analysis and optimization suggestions for operating data for manufacturing enterprises, helping them improve production efficiency and competitiveness.
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References
Amin, J.N.: Redefining the role of teachers in the digital era. Int. J. Indian Psychol. 3(3), 40–45 (2016)
Cao, Q., Yang, L., Ren, W., Song, Y., Huang, S., Wang, Y., Wang, Z.: Spatial distribution of harmful trace elements in Chinese coalfields: an application of WebGIS technology. Sci. Total. Environ. 755, 142527 (2021)
Correa, J.D.Y., Ricaurte, J.A.B.: Web application deveploment technologies using google web toolkit and google app engine-java. IEEE Lat. Am. Trans. 12(2), 372–377 (2014)
Cruz-Jesus, F., Oliveira, T., Bacao, F., Irani, Z.: Assessing the pattern between economic and digital development of countries. Inf. Syst. Front. 19, 835–854 (2017)
Desai, M., Shah, M.: An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and Convolutional neural network (CNN). Clin. Ehealth 4, 1–11 (2021)
Durmanov, A., Bartosova, V., Drobyazko, S., Melnyk, O., Fillipov, V.: Mechanism to ensure sustainable development of enterprises in the information space. Entrep. Sustain. 7, 1377–1386 (2019)
Fabregas, R., Kremer, M., Schilbach, F.: Realizing the potential of digital development: the case of agricultural advice. Science 366(6471), eaay3038 (2019)
Grieves, M.: Intelligent digital twins and the development and management of complex systems. Digit. Twin 2(8), 8 (2022)
Lee, J., Ni, J., Singh, J., Jiang, B., Azamfar, M., Feng, J.: Intelligent maintenance systems and predictive manufacturing. J. Manuf. Sci. Eng. 142(11), 110805 (2020)
Liu, J., Zhou, H., Liu, X., Tian, G., Wu, M., Cao, L., Wang, W.: Dynamic evaluation method of machining process planning based on digital twin. IEEE Access 7, 19312–19323 (2019)
Mahmudnia, D., Arashpour, M., Yang, R.: Blockchain in construction management: applications, advantages and limitations. Autom. Constr. 140, 104379 (2022)
Uchida, K., Tanaka, M., Okutomi, M.: Coupled convolution layer for convolutional neural network. Neural Netw. 105, 197–205 (2018)
Yang, D.L., Wang, J.L.: Research and application of SVG technology on the water injection well based on the data management platform. Appl. Mech. Mater. 321, 2503–2506 (2013)
Ye, L.J., Wang, S.H., Yu, X.C., Hu, Y.T., Wang, J.: Research on critical svg technology for low voltage high power load. Adv. Mater. Res. 1049, 716–719 (2014)
Zhang, H., Qi, Q., Tao, F.: A consistency evaluation method for digital twin models. J. Manuf. Syst. 65, 158–168 (2022)
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The first version was written by CL, SL and LY, all authors have proposed the idea, NL and WZ have done the simulations. All authors have contributed to the paper’s analysis, discussion, writing, and revision.
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Liu, C., Lin, S., Yao, L. et al. Research on intelligent operation and maintenance of power systems in manufacturing enterprises based on digital twin technology. Opt Quant Electron 56, 85 (2024). https://doi.org/10.1007/s11082-023-05713-9
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DOI: https://doi.org/10.1007/s11082-023-05713-9