基于自组织映射神经网络的电力用户负荷曲线聚类  

Power Customers Load Profile Clustering Using the SOM Neural Network

作　　者：李智勇[1] 吴晶莹[1] 吴为麟[1] 宋保明[2] 

机构地区：[1]浙江大学电气工程学院,浙江省杭州市310027 [2]北京市电力公司昌平供电公司,北京市102200

出　　处：《电力系统自动化》2008年第15期66-70,78,共6页Automation of Electric Power Systems

摘　　要：电力用户负荷曲线的聚类是形成合理电价体系和实施负荷管理措施的基础。文中基于自组织映射(SOM)神经网络进行低压终端用户的负荷曲线聚类研究。首先定义并提取功率曲线、分时功率、功率频谱3类向量,分别作为SOM神经网络的输入进行可视化聚类。采用相对量化误差和拓扑误差2个指标表征聚类质量,选取聚类结果最好的SOM输出层结合k均值法进行用户负荷曲线划分。根据Davies指标将所研究的131条曲线划分为8类,对每类曲线进行描述。最后进行新用户的识别,结果表明聚类方法有效、可靠。Power customers load profile clustering is the basis for constructing an appropriate tariff system and applying load management measures.This paper is concerned with the load profile clustering of low-voltage terminal customers using the self-organizing map（SOM） neural network.Three types of vectors,namely,the power curve,the time sharing power and the power spectrum are defined and used as inputs of the SOM neural network to realize clustering visualization.Two indices,the relative quantization error and topology error,are introduced to evaluate the clustering quality.After the assessment,the SOM output layer with optimal performance is selected to classify load profiles with the k-means method.The 131 profiles concerned are classified into eight types according to Davies index,with each group of profiles described.The new customer identifying ability of the SOM neural network is examined.The result shows that the method proposed is effective and reliable.

关 键 词：数据挖掘 电力用户 负荷曲线 聚类分析 自组织映射 

分 类 号：TM714[电气工程—电力系统及自动化] TP18[自动化与计算机技术—控制理论与控制工程]