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Power system bad load data detection based on an improved fuzzy C-means clustering algorithm

Authors: Zhengrong Wu; Xuzhu Dong; Zhiwen Liu; Xiangyu Kong; Yi Zeng; Qi'an Hu; Ying Chen;

Power system bad load data detection based on an improved fuzzy C-means clustering algorithm

Abstract

Massive electricity information are measured, and lots of bad data still exist. The mathematical method of hill-climbing to determine the initial clustering centers and clustering number was studied in this paper, which overcomes the subjectivity of determining clustering number and prevents the objective function from falling into local minimum. Then a bad data detection method is proposed, which combines the fuzzy C-means (FCM) clustering algorithm with hill-climbing method to determine the number of clusters and initial clustering centers in advance, cluster the load data, and finally generate a feasible region matrix by extracting the characteristic curve. At last, some cast studies are provided to demonstrate the effectiveness of the proposed method.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Average
Average
Average
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