Research on Power System Reliability Assessment based on Neural Network

DOI

10.2991/macmc-17.2018.99How to use a DOI?

Keywords

Power system, Reliability assessment, Rough set, Artificial neural network

Abstract

The number of components states combination and the power flow calculation are main causes producing "computation catastrophe" of reliability assessment calculation about composite generation and transmission systems. The input variables of artificial neural network are reduced, learning samples are extracted, stochastic events are roughly classified, a probable rule set about the relation between stochastic event classes and system states are draw out by means of rough set methods. A contingency pattern identification mode-Rough set and Neural Network (RNN), is presented. Furthermore, a power system reliability evaluation algorithm based on RNN is put forward for increasing the calculation speed of reliability assessment. The numerical experiments for reliability testing system s show the correctness, feasibility and usefulness of the presented method.

Copyright

© 2018, 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  - Jiashuo Liu
PY  - 2018/01
DA  - 2018/01
TI  - Research on Power System Reliability Assessment based on Neural Network
BT  - Proceedings of the 2017 4th International Conference on Machinery, Materials and Computer (MACMC 2017)
PB  - Atlantis Press
SP  - 532
EP  - 537
SN  - 2352-5401
UR  - https://doi.org/10.2991/macmc-17.2018.99
DO  - 10.2991/macmc-17.2018.99
ID  - Liu2018/01
ER  -