[1]黎灿兵,曲芳,王晓宁,等.基于模糊聚类的电力系统负荷特性分析[J].郑州大学学报(工学版),2010,31(01):110.[doi:10.3969/j.issn.1671-6833.2010.01.026]
Li Canbing,Qu Fang,WANG Xiaoning,et al.Analysis of power system load characteristics based on fuzzy clustering[J].Journal of Zhengzhou University (Engineering Science),2010,31(01):110.[doi:10.3969/j.issn.1671-6833.2010.01.026]
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基于模糊聚类的电力系统负荷特性分析(
)
《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]
- 卷:
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31卷
- 期数:
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2010年01期
- 页码:
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110
- 栏目:
-
- 出版日期:
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2010-01-30
文章信息/Info
- Title:
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Analysis of power system load characteristics based on fuzzy clustering
- 作者:
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黎灿兵; 曲芳; 王晓宁; 等.
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郑州大学电气工程学院,河南,郑州,450001
- Author(s):
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Li Canbing; Qu Fang; WANG Xiaoning; etc
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-
- DOI:
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10.3969/j.issn.1671-6833.2010.01.026
- 文献标志码:
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A
- 摘要:
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以变电站负荷构成比例作为基本特征向量,提出采用自适应矢量量化(AVQ)神经网络模糊聚类方法对电力负荷特性进行分类与综合.将AVQ神经网络模糊聚类方法和模糊C均值(FCM)聚妾方法进行了对比研究.通过对福建电网44个变电站进行聚类分析,得出两种聚类方法不仅具有良好的聚类综合能力,同时能够通过优化理论获得聚类中心矩阵,与其他聚类方法相比有明显优势;两者相比,AVQ神经网络模糊聚类算法本身机时小,处理速度更快,而且结果更为合理.
- Abstract:
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Taking the substation load composition ratio as the basic feature vector, the fuzzy clustering method of adaptive vector quantization (AVQ) neural network is proposed to classify and synthesize the power load characteristics. The fuzzy clustering method of AVQ neural network and the fuzzy C-mean (FCM) concubine method were compared. Through the cluster analysis of 44 substations of Fujian Power Grid, it is concluded that the two clustering methods not only have good clustering comprehensive ability, but also can obtain the clustering center matrix through optimization theory, which has obvious advantages compared with other clustering methods. Compared with the two, the AVQ neural network fuzzy clustering algorithm itself is small, the processing speed is faster, and the results are more reasonable.
更新日期/Last Update:
1900-01-01