基于模糊聚类-量子粒子群算法的用电特性识别

doi:10.3969/j.issn.1000-7229.2015.08.014

电力建设 ›› 2015, Vol. 36 ›› Issue (8): 84-88.doi: 10.3969/j.issn.1000-7229.2015.08.014

基于模糊聚类-量子粒子群算法的用电特性识别

郭昆亚1，熊雄2，金鹏1，孙芊3，井天军2

1.国网沈阳供电公司，沈阳市 110811；2.中国农业大学，北京市 100083；3.国网河南省电力公司电力科学研究院，郑州市 450052

出版日期:2015-08-01
作者简介:郭昆亚（1974），男，高级工程师，总工程师，主要从事电网调度、科技、通信等方面技术工作；熊雄（1988），男，博士研究生，主要研究方向为电力系统稳定与控制。
基金资助:
国家电网公司科技项目（SGLNSY00FZJS1401267）。

Electricity Characteristic Recognition Study Based on Fuzzy Clustering-Quantum Particle Swarm Algorithm

GUO Kunya1，XIONG Xiong2，JIN Peng1，SUN Qian3，JING Tianjun2

1. State Grid Shenyang Electric Power Supply Company, Shenyang 110811, China； 2. China Agricultural University, Beijing 100083, China； 3.State Grid Henan Electric Power Company, Zhengzhou 450052, China

Online:2015-08-01

摘要/Abstract

摘要：

为解决应用传统模糊C均值（fuzzy C-means, FCM）算法进行电力负荷模式提取时存在的对初始聚类中心敏感、聚类数目不易确定等问题，构建表征聚类效果的目标函数，并针对传统智能寻优算法易收敛、陷入局部最优等缺陷，采用一种量子编码的粒子群算法进行全局寻优以确定最佳聚类中心及分类数目，在确定最佳聚类中心及聚类数目基础上，构建能够全面反映各类型负荷的特征向量，最后通过与传统FCM算法下的计算结果进行对比，验证了该方法在用电识别方面的有效性及正确性。

关键词: 智慧城市, 负荷特性, 分类与综合, 量子粒子群算法, 模糊聚类

Abstract:

In allusion to such defects as sensitive to initial clustering center and not convenient to determine clustering number during utilizing traditional fuzzy C-Means （FCM） algorithm to extract power load patterns, this paper constructed objective function to reflect clustering effect, and used a quantum particle swarm algorithm for global optimization to determine the optimal clustering center and classification aiming at the defects of traditional intelligent optimization algorithm, such as easy convergence, falling into local optimum, etc. After determining the optimal clustering center and clustering number, the characteristics vector was constructed to fully reflect each kind of load. At last, by compared with the calculated results of traditional FCM algorithm, the effectiveness and correctness of the proposed algorithm in electricity recognition were verified.

Key words: smart city, load characteristic, classification and synthesis, quantum particle swarm algorithm, fuzzy clustering

中图分类号:

TM 714

郭昆亚，熊雄，金鹏，孙芊，井天军. 基于模糊聚类-量子粒子群算法的用电特性识别[J]. 电力建设, 2015, 36(8): 84-88.

GUO Kunya，XIONG Xiong，JIN Peng，SUN Qian，JING Tianjun. Electricity Characteristic Recognition Study Based on Fuzzy Clustering-Quantum Particle Swarm Algorithm[J]. ELECTRIC POWER CONSTRUCTION, 2015, 36(8): 84-88.

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基于模糊聚类-量子粒子群算法的用电特性识别

Electricity Characteristic Recognition Study Based on Fuzzy Clustering-Quantum Particle Swarm Algorithm

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