Proceedings of the 2016 International Conference on Civil, Transportation and Environment

Research Of Power System Load Model Based on Improved Genetic Programming

Authors
Jun Wang, Jian Zhang
Corresponding Author
Jun Wang
Available Online January 2016.
DOI
10.2991/iccte-16.2016.131How to use a DOI?
Keywords
power system,load model,genetic programming,automatic modeling.
Abstract

The model of the power system load has an important impact on power flow calculation,stability analysis.Compared with the traditional load modeling method to determine the structure further identification of the model parameters,using the genetic programming to load modeling do not have to preset the model structure.It automatically generates a different function from the input and output variables.According to their fitness looking for the most accurate fit function.In this paper,using the improved method of GP to load modeling.Precision and efficiency has been improved by optimize the adaptation of the calculation process.The effectiveness and feasibility of the improved genetic programming method is verified by compare with the traditional load modeling method.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2016 International Conference on Civil, Transportation and Environment
Series
Advances in Engineering Research
Publication Date
January 2016
ISBN
978-94-6252-185-8
ISSN
2352-5401
DOI
10.2991/iccte-16.2016.131How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Jun Wang
AU  - Jian Zhang
PY  - 2016/01
DA  - 2016/01
TI  - Research Of Power System Load Model Based on Improved Genetic Programming
BT  - Proceedings of the 2016 International Conference on Civil, Transportation and Environment
PB  - Atlantis Press
SP  - 768
EP  - 771
SN  - 2352-5401
UR  - https://doi.org/10.2991/iccte-16.2016.131
DO  - 10.2991/iccte-16.2016.131
ID  - Wang2016/01
ER  -