A Two-Step Clustering Algorithm Combined with Load Shape Index for Power Load Curve

doi:10.3969/j.issn.1000-7229.2016.06.014

Abstract

Abstract:

To make up for the drawback that clustering method based on Euclidean Distance considering all dimensions of load curves is weak in load shape similarity， this paper proposes a two-step clustering algorithm combined with load shape characteristic index for power load curve. First， this algorithm obtains the preliminary clustering result by using clustering method based on the Euclidean Distance of load curves and selects the clustering method and number through cluster evaluation index. Second， this algorithm uses supervised learning algorithm to reclassify load based on load shape characteristic index. Then， different clustering algorithms are compared. At last， we propose the suggestions for the application of the clustering results. The example results show that the proposed two-step clustering algorithm can make up for the weakness of traditional clustering algorithm in load shape similarity. Support vector machine （SVM） algorithm has better performance in the second-step classification. The proposed algorithm has practical significance.

Key words: load clustering, data mining for power system, load shape, supervised learning algorithm

CLC Number:

TM 714

PENG Bo， ZHANG Yi，XIONG Jun，DONG Shufeng，LI Yongjie. A Two-Step Clustering Algorithm Combined with Load Shape Index for Power Load Curve[J]. Electric Power Construction, 2016, 37(6): 96-102.

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