Comparison Study of the Clustering Analysis Methods in the Load Time-Variation Research

Abstract:

Load model has a great impact on the digital simulation result. In this paper, the measurement-based method is applied to model the load. If all the measured data are used for modeling respectively, the workload would be increased greatly. But if only one model is generated with the multi-curve fitting parameter identification method, the accuracy of modeling would be reduced greatly. The clustering analysis theory supplies an effective way to solve the problem above. There are some methods for clustering introduced in this paper. But a suitable method needs be studied firstly. The case study is presented to compare these methods. According to simulation result, it is concluded that the Kmeans method is best, while the usually adopted central clustering is actually not suitable for the load time-variation research.

• Periodical: Applied Mechanics and Materials (Volumes 548-549)
• Pages: 1135-1138
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.548-549.1135
• Online since: April 2014
• Authors: Yong Lin*, Yang Fan, Lie Xin Wu, Yan Hui Xu, Ge Song
• Keywords: Clustering Analysis, K-Means Method, Load Model, Load Time-Variation, Measurement-Based Method

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