摘要
运用数据挖掘中的聚类算法对电力负荷曲线进行聚类分析,提炼电力负荷曲线之间的共性特征与差异特征,在负荷模型实用化方面有重要应用价值,可以帮助分析用户用电规律,指导电网规划及实时调度。本文提出了一种自适应k-means++负荷特性聚类算法,综合不同聚类数时的聚类结果验证了数据集里各样本的相似性,通过迭代图切分的方法确定了最佳聚类数,避免了人为设定电力用户日负荷曲线聚类数不恰当导致的单一聚类结果的过大偏差,提高了负荷分类的精确性。算例结果验证了该算法的可行性和有效性,表明该算法求最佳聚类数的准确性高、鲁棒性好。
The clustering technique in data mining has been widely applied for load curves clustering.Load curves clustering helps refining common and different characteristics among loads,which has important application values for the practicality of load model.On the other hand,it helps analyzing load patterns,guiding planning and real-time dispatching of power systems.In this paper,an adaptive k-means++algorithm is proposed,which synthesizes results of different cluster numbers to verify the similarity of the samples in the dataset,and adopts an iterative graph-partitioning method to identify the optimal cluster number.The improved algorithm avoids excessive deviation of single clustering result caused by inappropriate cluster number of daily load curves,which could improve the accuracy of load curves classification.Numerical experiments verify the feasibility and effectiveness of the proposed algorithm,and show that the accuracy of algorithm is high and robustness is good when solving the best cluster number.
作者
李婧
徐胜蓝
万灿
卢奕城
王素英
LI Jing;XU Shenglan;WAN Can;LU Yicheng;WANG Suying(Shenzhen Power Supply Bureau Co.,Ltd.,Shenzhen,Guangdong 518000,China;College of Electrical Engineering,Zhejiang University,Hangzhou 310027,China;Shenzhen Power Supply Planning Design Institute Co.,Ltd.,Shenzhen,Guangdong 518000,China)
出处
《南方电网技术》
CSCD
北大核心
2019年第2期13-19,共7页
Southern Power System Technology
作者简介
李婧(1982),女,工程师,硕士,从事电网规划研究工作;徐胜蓝(1996),男,通信作者,硕士研究生,研究方向为电力负荷特性分析,shenglanxu@zju.edu.cn;万灿(1986),男,研究员、博士生导师,研究方向为电力系统能源预测技术、综合能源系统与主动配电网、人工智能及其智能电网应用,canwan@zju.edu.cn。
