基于DBSCAN二次聚类的配电网负荷缺失数据修补

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Abstract Distribution power load belongs to data with time series characteristics. According to the inherent regularity and fluctuation characteristics of the data, repairing the missing load data due to various factors can lay a foundation for the validity and predictability of the power system research and experimental results. Firstly, this paper proposes density-based spatial clustering of applications with noise (DBSCAN) secondary clustering method. Secondly, the load attribute similarity for distribution network load data is proposed, and the load record comprehensive similarity is further proposed. Thirdly, according to the load category results of DBSCAN secondary clustering method and the comprehensive similarity of the obtained load records, the data category with the largest similarity is matched, and the missing data is repaired. At last, the validity and correctness of the proposed method are proved by a numerical example.

Key words： density-based spatial clustering of applications with noise (DBSCAN) power load data similarity data repair

Received: 15 June 2021

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	Articles by authors
	CAI Wenbin
	CHENG Xiaolei
	WANG Peng
	WANG Yuan

Cite this article:

CAI Wenbin,CHENG Xiaolei,WANG Peng等. Repair of missing load data in distribution network based on DBSCAN secondary clustering[J]. Electrical Engineering, 2021, 22(12): 27-33.

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http://dqjs.cesmedia.cn/EN/Y2021/V22/I12/27

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