Anfeng Zhu, Zhao Xiao, Qiancheng Zhao*
Energy Engineering, Vol.118, No.3, pp. 549-563, 2021, DOI:10.32604/EE.2021.014177
Abstract Due to the frequent changes of wind speed and wind direction, the accuracy of wind turbine (WT) power prediction
using traditional data preprocessing method is low. This paper proposes a data preprocessing method which combines POT with DBSCAN (POT-DBSCAN) to improve the prediction efficiency of wind power prediction model.
Firstly, according to the data of WT in the normal operation condition, the power prediction model of WT is established based on the Particle Swarm Optimization (PSO) Arithmetic which is combined with the BP Neural Network
(PSO-BP). Secondly, the wind-power data obtained from the supervisory control and data acquisition (SCADA)
system… More >
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