基于自适应最佳聚类数目选择的改进KFCM变电站聚类算法OA
Substation clustering based on improved KFCM algorithm with adaptive optimal clustering number selection
变电站负荷组成查询和聚类分析是负荷建模的前提和基础.然而,传统的核模糊C-均值(KFCM)算法存在人为选择聚类数和容易收敛于局部最优解的弊端.为了克服这些限制,本文提出了一种基于自适应最佳聚类数选择的改进KFCM算法.该算法利用遗传算法强大的全局搜索能力和模拟退火算法强大的局部搜索能力对传统KFCM算法进行了优化,并通过聚类评价指标比率自适应地确定理想聚类数.与传统的KFCM算法相比,改进后的KFCM算法具有较强的聚类综合能力,能够有效地收敛到全局最优解.
The premise and basis of load modeling are substation load composition inquiries and cluster analyses.However,the traditional kernel fuzzy C-means(KFCM)algorithm is limited by artificial clustering number selection and its convergence to local optimal solutions.To overcome these limitations,an improved KFCM algorithm with adaptive optimal clustering number selection is proposed in this paper.This algorithm optimizes the KFCM algorithm by combining the …查看全部>>
徐衍会;高镱滈;成蕴丹;孙宇航;李雪松;潘险险;余浩
负荷变电站聚类模拟退火遗传算法核模糊C均值聚类算法聚类评价
Load substation clusteringSimulated annealing genetic algorithmKernel fuzzy C-means algorithmClustering evaluation
《全球能源互联网（英文）》 2023 (4)
505-516,12
This work was supported by the Planning Special Project of Guangdong Power Grid Co.,Ltd.:"Study on load modeling based on total measurement and discrimination method suitable for system characteristic analysis and calculation during the implementation of target grid in Guangdong power grid"(0319002022030203JF00023).
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