An Improved FCM Algorithm Based on Subtractive Clustering for Power Load Classification

Abstract:

FCM is used in many power load classification currently, but it also has some shortcomings. This paper give an algorithm based on Subtractive Clustering and improved Fuzzy C-means Clustering (SUB-FCM) to solve this problem. This algorithm use subtractive clustering to initialize the cluster center matrix, solve the random initialization of FCM, and improve the global search ability, avoid falling into local optima. Experimental analysis found this algorithm also could accelerate the convergence speed, and has better clustering results. It can be applied to power load classification effectively.

• Periodical: Advanced Materials Research (Volumes 986-987)
• Pages: 206-210
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.986-987.206
• Online since: July 2014
• Authors: Rui Dong*, Min Xiang Huang
• Keywords: Fuzzy C-Means Algorithm (FCM), Power Load Classification, Subtractive Clustering

References

[1] GUO Lianzhe, LI Li, TAN Zhongfu, et al. Time-of-use price design models based on fuzzy demand and users' diverse response[J]. East China Electric Power. 2007, 35(5): 11-15.

[2] LUO Ling. Research on categorized time-of-use power price based on fuzzy c-means clustering[D]. Shandong University, (2013).

[3] LI Peiqiang, LI Xinran, CHEN Huihua, et al. The characteristics classification and synthesis of power load based on fuzzy clustering[J]. Proceedings of the CSEE. 2005, 25(24): 73-78.

[4] Birch A P, Ozveren C S, Sapeluk A T. A generic load profiling technique using fuzzy classification[C]. Brighton, UK: Proceedings of Eighth International Conference on Metering and Tariffs for Energy Supply, 1996. 203-207.

DOI: 10.1049/cp:19960507

[5] LIU Li, WANG Gang, ZHAI Denghui. Application of K-means clustering algorithm in load curve classification[J]. Power System Protection and Control. 2011, 39(23): 65-68, 73.

[6] LI ZHiyong, WU Jingying, WU Weilin, et al. Power customers load profile clustering using the SOM neural network[J]. Automation of Electric Power Systems. 2008(15): 66-70.

[7] SUN Yaming, WANG CHenli, ZHANG ZHisheng, et al. Clustering analysis of power system load series based on ant colony optimization algorithm[J]. Proceeding of the CESS. 2005(18): 40-45.

[8] DUAN Ru, ZHANG Caiqing, LIU Aifang. Application of fuzzy clutering method in classification of electricity customers[J]. Power Demand Side Management. 2005, 7(5): 18-20.

[9] Prahastono I, King D J, Ozveren C S, et al. Electricity load profile classification using fuzzy c-means method[C]. 43rd International Universities Power Engineering Conference, 2008. 1-5.

DOI: 10.1109/upec.2008.4651527

[10] ZHOU Kaile, YANG SHanlin. An improved fuzzy c-means algorithm for power load characteristics classification[J]. Power System Protection and Control. 2012(22): 58-63.

[11] ZENG Bo, ZHANG Jianhua, DING Lan, et al. An improved adaptive fuzzy c-means algorithm for load characteristics classification[J]. Automation of Electric Power Systems. 2011(12): 42-46.

[12] XIAO chunJing, ZHANG Min. Research on fuzzy clustering based on subtractive clustering and fuzzy c-means[J]. Computer Engineering. 2005(S1): 135-137.

[13] YU Jian, YANG Minshen. Optimality test for generalized FCM and its application to parameter selection[J]. IEEE Transactions on Fuzzy Systems. 2005, 13(1).

DOI: 10.1109/tfuzz.2004.836065

[14] YANG Hao, ZHANG Lei, HE Qian, et al. Study of power load classification based on adaptive fuzzy C means[J]. Power System Protection and Control. 2010(16): 111-115.

[15] ZHANG Xun, DENG Huiwen. On the FCM clustering based on subtractive clustering and cluster validity evaluation[J]. Journal of Chongqing Institute of Technology. 2006(05): 59-62.

[16] FAN Jiulun, PEI Jihong, XIE Weixin. Cluster validity based on possibilistic distribution[J]. Acta Electronica Sinica. 1998(04): 113-115.