Power Load Characteristic Classification Technology Research Based on An Improved Fuzzy C-means Clustering Algorithm
[1]ZHAO Guosheng,NIU Zhenzhen,LIU Yongguang,et al.Power Load Characteristic Classification Technology Research Based on An Improved Fuzzy C-means Clustering Algorithm[J].Journal of Zhengzhou University (Engineering Science),2015,36(06):56-.[doi:10.3969/j.issn.1671 -6833.2015.06.01 1]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
36卷
Number of periods:
2015 06
Page number:
56-
Column:
Public date:
2015-12-25
- Title:
- Power Load Characteristic Classification Technology Research Based on An Improved Fuzzy C-means Clustering Algorithm
- Keywords:
- load clustering; FCM; load characteristic; daily load curve
- CLC:
- -
- DOI:
- 10.3969/j.issn.1671 -6833.2015.06.01 1
- Abstract:
- In view of the disadvantages of the traditional Fuy C-means clustering algorithm， the author pro-poses an adaptive FCM algorithm. This algorithm is based on two clustering results evaluation index of withinthe class distance MIA and between the class distance MDC. The ratio of MDC and MIA，defined as l, is anadaptive function to determine the clustering number c of FCM algorithm. At the same time，according to thefuzy decision method，we use the objective function and partition entropy of FCM algorithm together to deter-mine the value of optimal fuzzy weighted m. ’This algorithm not only overcomes the FCM algorithm disadvan-tage of not being able to determine the clustering number automatically and fuzzy weighted index needs to begiven by experience,but also the clustering result is optimal. Finally,the correctness and effectiveness of thealgorithm were proved through example analysis.
- References:
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[1]王冬利.电力需求侧管理实用技术M].北京:中国电力出版社,2005:7 - 14.[2]黄永皓,康重庆,夏清,等.用户分类电价决策方法的研究0﹒中国电力,2004,37( 1):24 -28.[3]徐明.基于负荷特性分析的错峰方案研究D]﹒广州:华南理工大学电力学院,2012:17 -19.
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