Short-term prediction of wind power based on self-adaptive niche particle swarm optimization

Hong Zhang^{1, 2}

COMPUTER MODELLING & NEW TECHNOLOGIES 2014 18(5) 168-173

¹ Department of Electrical Engineering, Southeast University, Nanjing, Jiangsu 210096 China

² Key Laboratory of Technical and Device Smart Grid, Nanjing, Jiangsu 210096 China

Connecting wind power to the power grid has recently become more common. To better manage and use wind power, its strength must be predicted precisely, which is of great safety and economic significance. Speed sensors are widely applied, it make prediction of wind power more accurate. In this paper, the short-term prediction of wind power is based on self-adaptive niche particle swarm optimization (NPSO) in a neural net. Improved PSO adopts the rules of classification and elimination of a niche using a self-adaptive nonlinear mutation operator. Compared with the traditional method of maximum gradient, NPSO can skip a local optimal solution and approach the global optimal solution more easily in practice. Compared with the basic PSO, the number of iterations is reduced when the global optimal solution is obtained. The method proposed in this paper is experimentally shown to be capable of efficient prediction and useful for short-term power prediction.