基于聚类的供电网格电源点选址定容方法
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引用本文:李楠1,谷凯凯2,顾然2,李妍3.基于聚类的供电网格电源点选址定容方法[J].电网与清洁能源,2022,38(5):10~15
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作者单位
李楠1 1. 国网湖北省电力有限公司荆门供电公司 
谷凯凯2 2. 国网电力科学研究院武汉南瑞有限责任公司 
顾然2 2. 国网电力科学研究院武汉南瑞有限责任公司 
李妍3 3. 华中科技大学 电气与电子工程学院 
基金项目:国网湖北省电力有限公司2021年配网规划需求成果评价咨询(20210572);国家重点研发计划“智能电网技术与装备”重点专项(2017YFB0902800)
中文摘要:供电网格直接服务于量大面广的中低压用户,其电源点选址定容是配电网结构优化的关键环节。该文基于大数据挖掘技术分析中低压负荷空间分布及其特征,提出一种基于聚类的供电网格电源点选址定容方法,方法应用有望减少规划工作收资和数据分析的工作量。该方法首先基于开源海量信息准确分析配电网负荷空间分布坐标,并定义局部密度[ρi]、样本间距[δi]两个指标用于刻画负荷的空间分布特征;采用聚类数据挖掘算法开展聚类优化,直到所有群簇集合的负荷均满足供电半径合理,负荷容量适当的约束条件,从而确定该区域的电源点位置和容量;通过聚类优选方法所得到的网格电源点平均供电半径最小,实现群簇内的电力用户线路损耗最小,体现该文电源点选址方法的经济性。最后结合实际地区的供电网格规划论证分析了其实用性。
中文关键词:负荷空间分布  聚类算法  选址定容  网格化规划
 
A Method for Power Source Location and Capacity Determination in Grids Based on Cluster Analysis
Abstract:The power supply grid directly serves a large number of medium and low voltage users, and the location and capacity of the power supply point is a key link in the optimization of the distribution network structure. This paper analyzes the spatial distribution and characteristics of medium and low voltage loads based on big data mining technology, and proposes a cluster-based power supply grid power point selection method. Firstly, based on the open source massive information, the spatial distribution coordinates of the distribution network load are accurately analyzed, and two indicators - the local density ρi and the sample spacing δi, are defined to describe the spatial distribution characteristics of the load; the clustering data mining algorithm is used to carry out cluster optimization until the load of the cluster set meets the constraints of reasonable power supply radius and appropriate load capacity, so as to determine the location and capacity of the power point in the area; the average power supply radius of the grid power point obtained by the clustering optimization method is the smallest, which realizes the power supply in the cluster. The power user line loss is the smallest, which reflects the economy of the power point location method proposed in this paper. Finally, this paper demonstrates the practicability of the proposed method in combination with the power grid planning of the actual area.
keywords:load space distribution  clustering algorithm  siting and sizing  grid planning
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