Research on Daily Load Curve Classification Based on Improved Fuzzy C-means Clustering Algorithm

For the problem of severe unpredictability and three-phase unbalance of user demand in low-voltage distribution networks, a daily load curve clustering technique integrating the sparrow search algorithm (SSA) and the fuzzy C-mean clustering algorithm (FCM) is presented. The initial load data set is...

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Bibliographic Details
Published in:Journal of physics. Conference series Vol. 2584; no. 1; pp. 12137 - 12142
Main Authors: Li, Mingcong, Hu, Heng, Shi, Haoran, Wang, Xi, Ye, Lan, Zhou, Feng
Format: Journal Article
Language:English
Published: Bristol IOP Publishing 01.09.2023
Subjects:
Algorithms
Classification
Clustering
Datasets
Optimization
Physics
Search algorithms
Unbalance
ISSN:1742-6588, 1742-6596
Online Access:Get full text
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Summary:For the problem of severe unpredictability and three-phase unbalance of user demand in low-voltage distribution networks, a daily load curve clustering technique integrating the sparrow search algorithm (SSA) and the fuzzy C-mean clustering algorithm (FCM) is presented. The initial load data set is pre-processed to lessen the interference of classification results. The load characteristic index of the daily load curve is removed to produce a reduced dimensional data set. The clustering validity index is introduced to solve the optimal number of clusters, and the early warning mechanism of the sparrow search algorithm is adopted to improve the global search capability. These improvements are made to improve the sensitivity of the initial clustering center of the FCM algorithm and the problem of local optimum in the process of finding the optimum. The simulation used to validate the revised clustering algorithm’s accuracy and efficacy for daily load categorization is a reference for resolving the three-phase load unbalance issue.
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ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2584/1/012137