TY - CONF
JO - Intelligent Systems (IS), 2012 6th IEEE International Conference
TI - Mean Variance Mapping Optimization for the identification of Gaussian Mixture Model: Test case
T2 - Intelligent Systems (IS), 2012 6th IEEE International Conference
IS -
SN -
VO -
SP - 158
EP - 163
AU - Gonzalez-Longatt, F.
AU - Rueda, J.
AU - Erlich, I.
AU - Villa, W.
AU - Bogdanov, D.
Y1 - 6-8 Sept. 2012
PY - 2012
KW - Load modeling
KW - Optimization
KW - Standards
KW - Substations
KW - Gaussian distribution
KW - load distribution
KW - normal distribution
KW - optimisation
KW - parameter estimation
KW - Gaussian mixture model identification
KW - MVMO approach
KW - convex combination
KW - load distributions
KW - load mode representation
KW - mean-variance mapping optimization algorithm
KW - normal distributions
KW - power system loads
KW - standard deviation
KW - Gaussian mixture Model
KW - Load Modeling
KW - Mean Variance Mapping Optimization Algorithm
KW - Optimization
VL -
JA - Intelligent Systems (IS), 2012 6th IEEE International Conference
DO - 10.1109/IS.2012.6335130
AB - This paper presents an application of the Mean-Variance Mapping Optimization (MVMO) algorithm to the identification of the parameters of Gaussian Mixture Model (GMM) representing variability of power system loads. The advantage of this approach is that different types of load distributions can be fairly represented as a convex combination of several normal distributions with respective means and standard deviation. The problem of obtaining various mixture components (weight, mean, and standard deviation) is formulated as a problem of identification and MVMO is used to provide an efficient solution in this paper. The performance of the proposed approach is demonstrated using two tests. Results indicate the MVMO approach is efficient to represented load models.
ER -