Conference Paper

Determination of representative load curve based on Fuzzy K-Means

Ho Chi Minh city Univ. of Technol., Ho Chi Minh City, Vietnam
DOI: 10.1109/PEOCO.2010.5559257 Conference: Power Engineering and Optimization Conference (PEOCO), 2010 4th International
Source: IEEE Xplore

ABSTRACT With the large amount of information (large number of daily load curves) for one consumer or one group of consumers, the classification and building the representative load curve (RLC) are necessary. The RLC can be built in the set of similar load curves by clustering analysis. This paper presents a Fuzzy clustering technique to determine RLC on the basis of their electricity behavior. Fuzzy K-Means (FKM) is utilized in this work. The load data used in this work are from actual measurements from different feeders derived from a distribution network. Global criterion method and Bellman-Zadeh's maximization principle will be used to compromise the Cluster validity indexes and determine the optimal cluster number. Determining the suitable weighting exponent m is also introduced in this paper.

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May 30, 2014