Effective Method for Wind and Solar Power Grid Systems Based on Recurrent Neural Networks
Keisuke Kimura, Takayuki Kimura, Takefumi Hiraguri,
and Kenya Jin’no
Nippon Institute of Technology, 4-1-1 Gakuendai, Miyashiro, Minami-Saitama, Saitama 345-8501, Japan
In this paper, the control method based on recurrent neural networks is proposed for optimizing large-scale wind and solar power generation systems. Recently, an optimal control method based on recurrent neural networks was proposed for wind and solar power generation systems. In this method, optimization problems are regarded as linear programming problems, which are solved by recurrent neural networks. Results suggest that this control method based on recurrent neural networks could be implemented in realworld systems. However, only small power generation systems were used to evaluate this control method in previous studies. Then, the method for power generation systems is evaluated by more realistic conditions. The results of our numerical experiments show that this control method delivers high performance with large-scale power generation systems. Furthermore, if the power generation systems has specific topologies, almost 20%of the supplying capacity is improved.
and Kenya Jin’no, “Effective Method for Wind and Solar Power Grid Systems Based on Recurrent Neural Networks,” J. Adv. Comput. Intell. Intell. Inform., Vol.18, No.6, pp. 1034-1043, 2014.
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