摘要
对海量负荷数据进行降维聚类处理是提取负荷关键信息,深度挖掘其内在规律的前提。根据负荷曲线的形态特征,文中提出了一种基于可变时间分辨率自适应分段聚合近似方法的曲线形态聚类算法。首先,根据负荷爬坡事件及基于斜率提取的边缘点来衡量负荷曲线的形态特征及其变化趋势,采用自适应分段聚合近似算法对用户日负荷数据集进行可变时间分辨率重构,进一步采用一种基于负荷曲线形态聚类的k-shape算法进行聚类处理,该聚类算法以一种基于曲线形态相似性的距离量度方式作为相似性判据,并依据斯坦纳树优化方法进行聚类中心计算。利用模拟数据、实测数据算例分析验证了所提算法在数据降维、负荷聚类中的实用性和有效性。
The dimension reduction and classification of massive load data are the premise of critical load information extraction and deep data mining.According to the shape characteristics of load curves,this paper proposes a shape clustering algorithm of load curves based on the adaptive piecewise aggregate approximation method with variable temporal resolution.The information of the ramp events and slope-extracted edge point is collected to quantify the shape characteristics and fluctuation level of load curves.Then the users’daily load data sets with variable temporal resolution are reconstructed by the adaptive piecewise aggregate approximation algorithm.Further,the k-shape algorithm is applied in the clustering of daily load curves according to the shape characteristics of load curves.In the k-shape algorithm,the distance measurement method based on the similarity of curve shape is regard as the similarity criterion,and the cluster center is calculated based on the Steiner’s sequence optimization method.The practicality and effectiveness of the proposed algorithm in data dimension reduction and load clustering are verified by simulation data and measured data.
作者
王潇笛
刘俊勇
刘友波
许立雄
马铁丰
胥威汀
WANG Xiaodi;LIU Junyong;LIU Youbo;XU Lixiong;MA Tiefeng;XU Weiting(College of Electrical Engineering and Information Technology,Sichuan University,Chengdu 610065,China;School of Statistic,Southwestern University of Finance and Economics,Chengdu 611130,China;Economic Research Institute of State Grid Sichuan Electric Power Company,Chengdu 610094,China)
出处
《电力系统自动化》
EI
CSCD
北大核心
2019年第1期110-118,共9页
Automation of Electric Power Systems
基金
国家自然科学基金资助项目(51437003)
国家重点研发计划资助项目(2017YFE0112600)~~
