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Abstract：
A method of identifying the operating parameters of harmonic customers based on historical monitoring data is introduced in this paper. Firstly, PCA is used to reduce the original harmonic data dimensions and determine the appropriate number of clusters. Then k-means is used to partition harmonic mode on low dimensions. Lastly group feature parameters are calculated from the clustered typical harmonic operating conditions. Experimental results showed that the operating parameters of harmonic customers can be identified from a large number of high-dimensional historical statistic data by the proposed method, which will also contribute to the harmonic source location and optimal operation strategy. © 2018 IEEE.
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Proceedings of 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2018
Year： 2019
Page： 101-106
Language： English
Cited Count：
SCOPUS Cited Count： 1
ESI Highly Cited Papers on the List： 0 Unfold All
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30 Days PV： 0
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