Cable Temperature Prediction Based on RF-GPR for Digital Twin Applications
Source abstract
Abstract
With the wide application of power cables in the field of transmission and distribution and the increasing emphasis of power departments on the reliability, safety and stability of power cable operation, how to more accurately and quickly analyze the temperature distribution of power cables and how to evaluate the running state of power cables have become research hotspots. Through the combination of finite element calculation and the artificial intelligence method, an innovative method of digital twin cable temperature prediction based on RF-GPR is proposed in this paper. Firstly, the finite element method is used to calculate the coupling of the electromagnetic field and temperature field of a 10 kV AC cable laid in the cable trench, and a certain amount of basic data are provided through the finite element calculation results. Then, using the basic principle of the random forest (RF) variable importance score, the RF-GPR cable temperature prediction model is constructed using the series hybrid model and Gaussian process regression (GPR), the model prediction results are compared and analyzed, and the calculation time is improved by about 1500 times. Finally, a digital twinning platform for cable temperature calculation based on RF-GPR is designed, which provides technical support for the application of digital twinning.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Electrical Fault Detection and Protection · Thermal Analysis in Power Transmission · Electrical Contact Performance and Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
State Grid Handan Electric Power Supply Company, Handan 056000, China
Hebei Provincial Key Laboratory of Power Transmission Equipment Security Defense, North China Electric Power University, Baoding 071000, China
Funding
the Science and Technology Project of State Grid Hebei Electric Power Company
References (32)
Show source-provided references
1. Key technologies and application prospects of digital twin in power equipment industry
2. Shaping the Digital Twin for Design and Production Engineering10.1016/j.cirp.2017.04.040 ↗
3. A Survey on Digital Twin: Definitions, Characteristics, Applications, and Design Implications10.1109/ACCESS.2019.2953499 ↗
4. Setting up a Digital Twin Assisted Greenhouse Architecture10.3844/ajeassp.2022.230.238 ↗
5. Chaux, J.D., Sanchez-Londono, D., and Barbieri, G. (2021). A Digital Twin Architecture to Optimize Productivity within Controlled Environment Agriculture. Appl. Sci., 11.10.3390/app11198875 ↗
6. Piromalis, D., and Kantaros, A. (2022). Digital Twins in the Automotive Industry: The Road toward Physical-Digital Convergence. Appl. Syst. Innov., 5.10.3390/asi5040065 ↗
7. Development and operation of Digital Twins for technical systems and services10.1016/j.cirp.2019.04.024 ↗
8. Application Exploration of Digital Twin Technology in High Voltage Cable Field
9. Application Status and Prospect of Digital Twin Technology in State Evaluation of Power Transmission and Transformation Equipment
10. MES-integrated digital twin frameworks10.1016/j.jmsy.2020.05.007 ↗
11. Kantaros, A., Piromalis, D., Tsaramirsis, G., Papageorgas, P., and Tamimi, H. (2022). 3D Printing and Implementation of Digital Twins: Current Trends and Limitations. Appl. Syst. Innov., 5.10.3390/asi5010007 ↗
12. Research on wind turbine modeling based on digital twin technology
13. Real-time online analysis of power grid
14. Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison10.1109/ACCESS.2018.2793265 ↗
15. Non-Concentric Ladder Soil Model for Dynamic Rating of Buried Power Cables10.1109/TPWRD.2020.2977052 ↗
16. Concept, Characteristics and Application Prospect of Digital twinning in Power System
17. A CNN recognition method for early stage of 10 kV single core cable based on sheath current10.1016/j.epsr.2020.106292 ↗
18. Feature extraction and severity assessment of partial discharge under protrusion defect based on fuzzy comprehensive evaluation10.1049/iet-gtd.2015.0403 ↗
19. Prediction method for power transformer running state based on LSTM network
20. Finite control set model predictive control approach of nine switch inverter-based drive systems: Design, analysis, and validation10.1016/j.isatra.2020.10.037 ↗
21. A novel RNN based load modelling method with measurement data in active distribution system10.1016/j.epsr.2018.09.006 ↗
22. Calculation of core temperature of Single-core cable Joint based on Finite Element Method
23. Wu, J., Fang, L.C., and Liu, X.T. (2023). SOH estimation of lithium ion battery based on feature optimization and Random Forest algorithm. J. Mech. Eng., 1–9.
24. Application of Improved Random Forest Algorithm in Motor Bearing Fault Diagnosis
25. Han, F.J., Wang, X.H., Qiao, J., Shi, M.J., and Pu, T.J. (2023). A review of Load forecasting for new Power Systems based on Artificial Intelligence. Proc. Chin. Soc. Electr. Eng., 1–24.
26. Short-Term Electric Load Forecasting with Sparse Coding Methods10.1109/ACCESS.2021.3098121 ↗
27. Machine Learning Based Cost Effective Electricity Load Forecasting Model Using Correlated Meteorological Parameters10.1109/ACCESS.2020.3014086 ↗
28. Short-term Power Load Prediction based on Time Convolutional Network
29. General Harmonic Source Uncertainty Model based on Gaussian Process Regression
30. A Diagnosis method of zero drift of wind turbine yaw Angle Based on isolated forest and Sparse Gaussian Process Regression
31. Survey on Characteristics, Architecture and Applications of Digital Twin Power Grid
32. Construction method of digital twin battery for intelligent management