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1.浙江大学电气工程学院,浙江省杭州市 310027
2.国网浙江省电力有限公司电力科学研究院,浙江省杭州市 310014
3.国网浙江省海盐县供电有限公司,浙江省嘉兴市 314300
4.浙江华云信息科技有限公司,浙江省杭州市 310012
Received:21 June 2019,
Revised:2019-09-28,
Published Online:26 February 2020,
Published:25 April 2020
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连子宽,姚力,刘晟源等.基于t-SNE降维和BIRCH聚类的单相用户相位及表箱辨识[J].电力系统自动化,2020,44(8):176-184.
LIAN Zikuan,YAO Li,LIU Shengyuan,et al.Phase and Meter Box Identification for Single-phase Users Based on t-SNE Dimension Reduction and BIRCH Clustering[J].Automation of Electric Power Systems,2020,44(8):176-184.
连子宽,姚力,刘晟源等.基于t-SNE降维和BIRCH聚类的单相用户相位及表箱辨识[J].电力系统自动化,2020,44(8):176-184. DOI: 10.7500/AEPS20190621001.
LIAN Zikuan,YAO Li,LIU Shengyuan,et al.Phase and Meter Box Identification for Single-phase Users Based on t-SNE Dimension Reduction and BIRCH Clustering[J].Automation of Electric Power Systems,2020,44(8):176-184. DOI: 10.7500/AEPS20190621001.
低压台区单相用户的相位及接入表箱信息的准确性对户变关系纠错和线损治理分析有重要影响。目前,拓扑档案的校验主要依靠电力员工现场排查,人力物力消耗大且排查效率低下。因此,亟需一种效率较高的低压台区拓扑档案校验方法。在此背景下,文中提出了一种基于智能电表电压数据的低压台区单相用户相位及接入表箱辨识方法,可以为低压台区的拓扑辨识及排查提供参考。首先,采用
t
分布的随机近邻嵌入(
t
-SNE)技术对原始负荷数据进行降维处理,解决台区用户原始负荷特征维度过高带来的冗余性问题;接着,应用BIRCH方法对降维后的负荷数据进行聚类,实现台区下单相用户所属相位和接入表箱的辨识。最后,以浙江省海宁市某台区为例进行验证,算例分析的结果表明所提模型具有可行性和有效性。
The accurate phase and meter box information of single-phase users in low-voltage courts have great impacts on the checking of user-transformer relationships and the treatment and analysis of line losses. At present
the correction of topological documents mainly relies on the on-site checking by electrical engineers
which spends a lot of manpower and material resources with low efficiency. Therefore
it is necessary to study a more efficient method of checking the topological documents of the low-voltage courts. In this background
a phase and meter box identification method based on voltage measurement data of smart meters is proposed
which provides reference for topology identification and correction of low-voltage courts. Firstly
the
t-
distributed stochastic neighbor embedding (
t
-SNE) technology is adopted to reduce the dimension of original load data
so as to solve the redundancy problems caused by excessively high dimension of original load characteristics of users. Then
the balanced iterative reducing and clustering using hierarchies (BIRCH) method is used to cluster the dimension-reduced load data
so as to identify the phase and meter box information of single-phase users. Finally
case studies for an actual low-voltage court in Haining of Zhejiang Province
China
are performed to verify the correctness of the proposed method
and the results show that the proposed model is feasible and effective.
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