Open Access

ARTICLE

Power Data Preprocessing Method of Mountain Wind Farm Based on POT-DBSCAN

Anfeng Zhu, Zhao Xiao, Qiancheng Zhao^*

Engineering Research Center of Hunan Province for the Mining and Utilization of Wind Turbines Operation Data, Hunan University of Science and Technology, Xiangtan, 411201, China

* Corresponding Author: Qiancheng Zhao. Email:

(This article belongs to the Special Issue: Wind Energy Development and Utilization)

Energy Engineering 2021, 118(3), 549-563. https://doi.org/10.32604/EE.2021.014177

Received 07 September 2020; Accepted 20 October 2020; Issue published 22 March 2021

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 is preprocessed by the POT-DBSCAN method. Then, the power prediction of the preprocessed data is carried out by PSO-BP model. Finally, the necessity of preprocessing is verified by the indexes. This case analysis shows that the prediction result of POT-DBSCAN preprocessing is better than that of the Quartile method. Therefore, the accuracy of data and prediction model can be improved by using this method.

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