• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus
  • Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
  • Chinese S&T Journal Citation Reports
  • Chinese Science Citation Database
Volume 55 Issue 1
Jan.  2020
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Article Contents
ZHANG Liyan, CHEN Yingyue, HAN Zhengqing. Traction Load Classification Method Based on Improved Clustering Method[J]. Journal of Southwest Jiaotong University, 2020, 55(1): 27-33, 40. doi: 10.3969/j.issn.0258-2724.20180513
Citation: ZHANG Liyan, CHEN Yingyue, HAN Zhengqing. Traction Load Classification Method Based on Improved Clustering Method[J]. Journal of Southwest Jiaotong University, 2020, 55(1): 27-33, 40. doi: 10.3969/j.issn.0258-2724.20180513

Traction Load Classification Method Based on Improved Clustering Method

doi: 10.3969/j.issn.0258-2724.20180513
  • Received Date: 09 Jul 2018
  • Rev Recd Date: 11 Oct 2018
  • Available Online: 13 Nov 2018
  • Publish Date: 01 Feb 2020
  • In order to obtain more accurate traction load classification, based on a large amount of measured traction load data, an improved fuzzy C-means clustering method is proposed, which can automatically obtain the best classification number. A charged effective coefficient, the maximum value, the average value, the value of 95% and one to five order moments were chosen as clustering indicators to classify feeder current. Then the probability density function of traction loads was fitted using non-parametric kernel density estimation, and the probability distribution model of each feeder current type was obtained. The results show that the characteristic parameters and probability distributions of the traction loads that were clustered together are.

     

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