Power Load Classification Based on Feature Weighted Fuzzy Clustering

doi:10.11930/j.issn.1004-9649.202006320

Abstract

Abstract: The load classification of power users provides basic guidance for the research of power system planning, load forecasting, and time-of-use electricity price. In this paper, the variational modal decomposition(VMD) and fuzzy C-means clustering algorithm(FCM) are used for power load classification. Based on the unique feature weight of Euclidean distance in FCM, a feature weighting based VMD-FCM clustering algorithm is proposed using the feature weighting based fuzzy clustering method. According to the measured load data of the power grid, the VMD method can effectively decompose the inherent modality of the data, and the introduced FCM-based weight coefficient significantly improves the algorithm's convergence speed and clustering accuracy. The clustering results show that the proposed VMD-FCM clustering method can effectively distinguish different load types and has practical application values, thereby providing guidance for the design and planning of the power system.

Key words: load classification, fuzzy clustering, variational mode decomposition, feature weighting, load characteristic curve

MA Zongbiao, XU Su'an, ZHU Shaobin, WANG Jing. Power Load Classification Based on Feature Weighted Fuzzy Clustering[J]. Electric Power, 2022, 55(6): 25-32.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202006320

https://www.electricpower.com.cn/EN/Y2022/V55/I6/25

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