Research on Optimization of Generator Grid-Related Parameters Based on Double-Stranded Quantum Genetic Algorithm

DOI

10.2991/aiie-16.2016.30How to use a DOI?

Keywords

generator grid-related parameters; grid partitioning; island operation; transient stability; double-stranded quantum genetic algorithm; machine-network coordination

Abstract

With the expansion of grid scale and the development of UHV technology, grid partitioning mode is more and more widely applied. While the grid is closely connected with the large power grid, there is still the risk of separation and isolation mode operation. Under this background, this paper carries on the optimization research to the generation-network coordination parameters of generator set in the partitioned power network, analyzes the grid-related parameters of generator set and selects the excitation system regulator gain and the steam turbine governing system speed deviation amplification gain as the parameters optimization object. In addition, this paper takes transient stability optimization as the optimization target, deduces the objective function which shows the transient stability and establishes the corresponding optimization model. The model is solved by double-stranded quantum genetic algorithm. Finally, a practical example of the Z-S district power system of the J regional grid verifies the feasibility and validity of the proposed model.

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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TY  - CONF
AU  - Yan Xu
AU  - Lixin Fan
AU  - Yunqian Li
AU  - Zhong Chen
PY  - 2016/11
DA  - 2016/11
TI  - Research on Optimization of Generator Grid-Related Parameters Based on Double-Stranded Quantum Genetic Algorithm
BT  - Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)
PB  - Atlantis Press
SP  - 122
EP  - 128
SN  - 1951-6851
UR  - https://doi.org/10.2991/aiie-16.2016.30
DO  - 10.2991/aiie-16.2016.30
ID  - Xu2016/11
ER  -