Impact of Model Parameter Reduction on Load Classification

doi:10.11930/j.issn.1004-9649.2013.11.47.4

Abstract

Abstract: As one of the important components in power simulation, load model has been getting more and more attention. But current load models have lots of parameters, which significantly affects the model's identification accuracy. To solve this problem, the common practice is to reduce the parameters and load classification is always utilized to eliminate the time-dependent characteristics of the power load. However, it is not known yet how the parameter reduction effects the load classification. Cluster analysis is firstly carried out in this paper to classify the load data, and then the trajectory sensitivity analysis is utilized to reduce the model parameters. Reclassification of the load data is then made after parameter reduction and a comparison is conducted between the classification results before and after parameter reduction. A case study indicates that the parameter reduction doesn't increase the classification error significantly and proves the efficiency of the proposed parameter reduction method.

Key words: load model, parameter reducing, load classification, cluster analysis, trajectory sensitivity

CLC Number:

TM714

ZHENG Xiao-yu, ZHENG Jing-yuan, LIU Dong, WAN Xiong, LIANG Zhi-feng, LIU Li-hua. Impact of Model Parameter Reduction on Load Classification[J]. Electric Power, 2013, 46(11): 47-51.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.2013.11.47.4

https://www.electricpower.com.cn/EN/Y2013/V46/I11/47

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