| Issue |
E3S Web Conf.
Volume 118, 2019
2019 4th International Conference on Advances in Energy and Environment Research (ICAEER 2019)
|
|
|---|---|---|
| Article Number | 01004 | |
| Number of page(s) | 5 | |
| Section | Energy Engineering, Materials and Technology | |
| DOI | https://doi.org/10.1051/e3sconf/201911801004 | |
| Published online | 04 October 2019 | |
Temperature Prediction of Power Cable Joint Based on LS-SVM Optimized by PSO
State Grid Shanghai Cable Company, 200072 Shanghai, China
* Corresponding author: hebangle@sina.com
The temperature of high-voltage cable has a great significance to reflect the operation status, and the accurate prediction of the joint temperature can improve the safe operating level of the wire. This paper points out a temperature prediction model based on least squares support vector machine (LS-SVM) to forecast short-term cable joint temperature. This paper also conducts a test on a Shanghai 110kV cable line with its joint’s history temperature, environmental temperature and humidity, the wire core/sheath current ratio data, and the particle swarm optimization algorithm (PSO) can be adapted to optimize model parameter standardization and regularization parameter dynamically. The results prove that method can predict the temperature of cable joint with high prediction accuracy and also provide a reliable basis for cable temperature detection and early warning system.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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