On-line Load Estimation for Distribution Automation Using AMI Data

被引：0

作者：

Singh, Urvir ^{[1
]}

Zamani, Vahraz ^{[1
]}

Baran, Mesut ^{[1
]}

机构：

[1] North Carolina State Univ, ECE Dept, Raleigh, NC 27606 USA

来源：

2016 IEEE POWER AND ENERGY SOCIETY GENERAL MEETING (PESGM) | 2016年

关键词：

AMI; clustering; distribution systems; load estimation; k-means method; regression; time series; DISTRIBUTION-SYSTEMS;

D O I：

暂无

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

This paper presents a method to estimate the loads on a distribution feeder in real-time with limited real-time customer load data from AMI. The method consists of two modules: a load clustering module and a load modeling module. Load clustering helps to identify customer loads with similar profiles thereby helping to determine the subset of customers that need to be monitored in real-time. The load modelling module constructs statistical models for each load on the feeder. The module uses both the historical AMI data and the real-time AMI data from the selected customers in order to improve the performance of the model. Test results based on actual AMI data show the effectiveness of the method.

引用

页数：5

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