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Research Article

Academia Journal of Scientific Research 7(7): 381-388, July 2019
DOI: 10.15413/ajsr.2019.0706
ISSN: 2315-7712
2019 Academia Publishing 

Abstract

 


Temperature prediction of power cable joint based on LS-SVM optimized by PSO
 

 

Accepted 26th July, 2019

 
Bang-Le HE

State Grid Shanghai Cable Company, 200072 Shanghai, China.

 

The temperature of high-voltage cable has a great significance in reflecting 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 110 kV 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 this 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.

Key words: Temperature prediction model, least squares support vector machine (LS-SVM), PSO.

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Cite this article as:
HE BL (2019). Temperature prediction of power cable joint based on LS-SVM optimized by PSO. Acad. J. Sci. Res. 7(7): 381-388.

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