Operation State Prediction in Wind Power Integrated Systems Based on Artificial Neural Network

DOI

10.2991/iea-15.2015.101How to use a DOI?

Keywords

wind power; neural network; operation state prediction; elastic back-propagation algorithm; PMU

Abstract

With the capacity of integrated wind farm increasing, the reliability issues of power systems could not be ignored. This paper proposes an evaluation method for power system operation state based on elastic back-propagation neural network through the data of the phasor measurement unit. The effectiveness of the proposed method is verified by the IEEE 14-bus system, it has overcome the slow convergence rate problem and the prediction accuracy is acceptable. Condition assessment of power systems operation state is an important approach to improving the operation reliability of power systems.

Copyright

© 2015, 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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TY  - CONF
AU  - Jiang Wang
AU  - Lu Jiping
PY  - 2015/09
DA  - 2015/09
TI  - Operation State Prediction in Wind Power Integrated Systems Based on Artificial Neural Network
BT  - Proceedings of the AASRI International Conference on Industrial Electronics and Applications (2015)
PB  - Atlantis Press
SP  - 414
EP  - 417
SN  - 2352-5401
UR  - https://doi.org/10.2991/iea-15.2015.101
DO  - 10.2991/iea-15.2015.101
ID  - Wang2015/09
ER  -