Traction Load Classification Method Based on Improved Clustering Method

ZHANG Liyan; CHEN Yingyue; HAN Zhengqing

doi:10.3969/j.issn.0258-2724.20180513

Volume 55 Issue 1

Jan. 2020

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Journal of Southwest Jiaotong University > 2020 > 55(1): 27-33, 40.

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

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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

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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

Abstract

Abstract

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.
- traction load classification,
- fuzzy C-means clustering method,
- probability density function,
- non-parametric estimation

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References

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