Reactive power optimizational configuration of wind farms on the distribution network based on improved random black hole particle swarm optimization algorithm
Chaofan Zong, Zhongxiao Cong, Zhigang Lu
Available Online April 2015.
- https://doi.org/10.2991/amcce-15.2015.87How to use a DOI?
- wind farms of the distribution network; Weibull model; reactive power limit; optimal installation location; optimal reactive power generation; improved random black hole particle swarm optimization algorithm
- At present, the reactive power compensation devices of the distribution network can’t compensate reactive power smoothly. To solve this problem, this paper studys reactive power optimizational generation of wind farms on the distribution network. First of all, in order to reflect the wind property,We adopt Weibull wind farm probabilistic model.Thus, the mathematical expected power of the model is the active power of double-fed induction wind turbine(DFIG) .In addition, we take the DFIG reactive power limit into account. Last but no least, we present a improved random black hole particle swarm optimization algorithm(IRBHPSO) which is faster convergence, better global searching. Using this algorithm, we can easily solve the optimal installation location and the optimal reactive power generation capacity of wind farms. We do a simulation on the IEEE33 system which has the same wind conditions on every node and compare IRBHPSO with other four algorithms. Finally, the result shows that the proposed algorithm is effective and feasible, and the installation of wind farms can enhance the power quality of the distribution network.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Chaofan Zong AU - Zhongxiao Cong AU - Zhigang Lu PY - 2015/04 DA - 2015/04 TI - Reactive power optimizational configuration of wind farms on the distribution network based on improved random black hole particle swarm optimization algorithm BT - 2015 International Conference on Automation, Mechanical Control and Computational Engineering PB - Atlantis Press SN - 1951-6851 UR - https://doi.org/10.2991/amcce-15.2015.87 DO - https://doi.org/10.2991/amcce-15.2015.87 ID - Zong2015/04 ER -