Proceedings of the 3rd International Conference on Mechatronics, Robotics and Automation

A Statistical Selection Approach of Dynamic Load Model Parameters

Authors
Yulong Huang, Xun Chen
Corresponding Author
Yulong Huang
Available Online April 2015.
DOI
https://doi.org/10.2991/icmra-15.2015.135How to use a DOI?
Keywords
Load model; Time-variation; Multiple linear regression
Abstract
Since real load characteristics are time-varying all along, load model parameters built on some historical data are only valid within limited scenarios. A real-time dynamic load model parameter selection method based on multiple linear regression (MLR) is proposed to find the load model parameters that are best matched with real time system operation condition from load model parameters history database. And along with the database size growing larger, the load model parameter matching accuracy will become higher and higher. The effectiveness and accuracy of the proposed method are verified on field measurement data collected from a substation in a metropolitan area of China.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
3rd International Conference on Mechatronics, Robotics and Automation
Part of series
Advances in Computer Science Research
Publication Date
April 2015
ISBN
978-94-62520-76-9
ISSN
2352-538X
DOI
https://doi.org/10.2991/icmra-15.2015.135How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Yulong Huang
AU  - Xun Chen
PY  - 2015/04
DA  - 2015/04
TI  - A Statistical Selection Approach of Dynamic Load Model Parameters
BT  - 3rd International Conference on Mechatronics, Robotics and Automation
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmra-15.2015.135
DO  - https://doi.org/10.2991/icmra-15.2015.135
ID  - Huang2015/04
ER  -