Statistical Representation of Distribution System Loads Using Gaussian Mixture Model
File(s)
Published version
Author(s)
Singh, R
Pal, BC
Jabr, RA
Type
Journal Article
Abstract
This paper presents a probabilistic approach for statistical modeling of the loads in distribution networks. In a distribution network, the probability density functions (pdfs) of loads at different buses show a number of variations and cannot be represented by any specific distribution. The approach presented in this paper represents all the load pdfs through Gaussian mixture model (GMM). The expectation maximization (EM) algorithm is used to obtain the parameters of the mixture components. The performance of the method is demonstrated on a 95-bus generic distribution network model.
Version
Published version
Date Issued
2010
Citation
IEEE Transactions on Power Systems, 2010, 25 (1), pp.29-37
ISSN
0885-8950
Publisher
IEEE
Start Page
29
End Page
37
Journal / Book Title
IEEE Transactions on Power Systems
Volume
25
Issue
1
Copyright Statement
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Source Volume Number
25
Subjects
load modelling
pseudo measurements
state estimations
power distribution system
gaussian mixture model
Expectation maximization
Coverage Spatial
USA