@article{Manitsas:2012,
author = {Manitsas, E and Singh, R and Pal, BC and Strbac, G},
journal = {IEEE Transactions on Power Systems},
pages = {1888--1896--1896},
title = {Distribution System State Estimation using an Artificial Neural Network Approach for Pseudo Measurement Modeling},
url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6176289&refinements%3D4291944246%2C4291944245%26ranges%3D2012_2014_p_Publication_Year%26matchBoolean%3Dtrue%26searchField%3DSearch_All%26queryText%3D%28p_Authors%3Apal+b.+c.%29},
volume = {27},
year = {2012}
}

Download

TY  - JOUR
AB  - This paper presents an alternative approach topseudo measurement modeling in the context of distributionsystem state estimation (DSSE). In the proposed approach pseudomeasurements are generated from a few real measurements usingartificial neural networks (ANNs) in conjunction with typicalload profiles. The error associated with the generated pseudomeasurements is made suitable for use in the weighted leastsquares (WLS) state estimation by decomposition into severalcomponents through the Gaussian mixture model (GMM). Theeffect of ANN-based pseudo measurement modeling on thequality of state estimation is demonstrated on a 95-bus sectionof the U.K. generic distribution system (UKGDS) model
AU  - Manitsas,E
AU  - Singh,R
AU  - Pal,BC
AU  - Strbac,G
EP  - 1896
PY  - 2012///
SN  - 0885-8950
SP  - 1888
TI  - Distribution System State Estimation using an Artificial Neural Network Approach for Pseudo Measurement Modeling
T2  - IEEE Transactions on Power Systems
UR  - http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6176289&refinements%3D4291944246%2C4291944245%26ranges%3D2012_2014_p_Publication_Year%26matchBoolean%3Dtrue%26searchField%3DSearch_All%26queryText%3D%28p_Authors%3Apal+b.+c.%29
VL  - 27
ER  - 

Download