| 一种基于改进模糊聚类算法的自适应典型日选取方法 |
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| 引用本文： | 邬浩泽1,朱晨烜1,张贻山1,龙艳花2. 一种基于改进模糊聚类算法的自适应典型日选取方法[J]. 陕西电力, 2022, 0(1): 60-67 |
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| 作者姓名： | 邬浩泽1 朱晨烜1 张贻山1 龙艳花2 |
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| 作者单位： | （1.上海电机学院电气学院，上海 201306；2.上海师范大学信息与机电工程学院，上海 200234） |
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| 摘 要： | 考虑单一算法在选取典型日负荷曲线上的不足，将改进后的可能模糊C均值聚类算法（PFCM）与模糊线性判别法（FLDA）相结合提出一种新的集成聚类方法。首先将原有的PFCM改进，得到改进后的PFCM，并将其应用于最佳聚类数的选取；然后将改进后的PFCM与FLDA相结合，将该集成聚类算法应用于负荷曲线的聚类。最后，通过某电网全年负荷数据验证了所提方法在典型日选取上的有效性。
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| 关 键 词： | 自适应 模糊聚类 特征指标降维 模糊线性判别 典型日选取 |
Adaptive Method for Selecting Typical Days Based on Improved Fuzzy Clustering Algorithm |
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| WU Haoze1,ZHU Chenxuan1,ZHANG Yishan1,LONG Yanhua2. Adaptive Method for Selecting Typical Days Based on Improved Fuzzy Clustering Algorithm[J]. Shanxi Electric Power, 2022, 0(1): 60-67 |
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| Authors: | WU Haoze1 ZHU Chenxuan1 ZHANG Yishan1 LONG Yanhua2 |
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| Affiliation: | （1. School of Electrical Engineering， Shanghai Dianji University， Shanghai 201306， China； 2. School of Information and Electromechanical Engineering， Shanghai Normal University， Shanghai 200234， China） |
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| Abstract: | Considering the deficiency of single algorithm in selecting typical daily load curve， this paper proposes a new integrated clustering method by combining the improved possible fuzzy C-means （PFCM） algorithm with fuzzy linear discriminant analysis（FLDA）. Firstly， the original PFCM is improved to get the improved PFCM， and it is applied to the selection of the optimal number of clusters. Then the improved PFCM is combined with FLDA， and the integrated clustering algorithm is applied to the clustering of the load curves. Finally， the validity of this method in selecting the typical days is verified by the annual load data of a power grid. |
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| Keywords: | self-adaptation fuzzy clustering dimensionality reduction of feature index fuzzy linear discriminant analysis typical day selection |
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