Transformer Top-oil Temperature Modeling Based on Kernel-based Extreme Learning Machine

被引：0

作者：

Huang, Hua ^{[1
]}

Wei, Ben-gang ^{[1
]}

Qi, Xiao-wu ^{[2
]}

Xu, Yan-shun ^{[2
]}

Hu, Shuang ^{[2
]}

Sun, Kai-qi ^{[2
]}

Wang, Mei-yan ^{[2
]}

Guo, Jing ^{[2
]}

机构：

[1] Shanghai Municipal Elect Power Co EPRI, Equipment State Evaluat Ctr, Shanghai 200437, Peoples R China

[2] Shandong Univ, Sch Elect Engn, Jinan 250061, Shandong, Peoples R China

来源：

2016 INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING AND AUTOMATION (ICEEA 2016) | 2016年

关键词：

Power transformer; Top-oil temperature; Kernel-based extreme learning machine; Particle swarm optimization;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Transformer top-oil temperature (TOT) and winding hot-spot temperature (HST) are key indices to evaluate thermal condition of transformers. In order to improve TOT prediction accuracy, a TOT prediction model based on kernel-based extreme learning machine is established and particle swarm optimization algorithm is adopted to train the model and optimize the kernel parameters. The proposed model is tested on a 50MVA 110/37kV ONAN transformer. Besides, to verify the advantages of the proposed model, it's compared with several traditional data-driven models. The results demonstrate the validity and accuracy of the proposed model.

引用

页数：6

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