Construction of wind turbine performance model based on SCADA data. Issue 1 (March 2021)

• Record Type: Journal Article
• Authors: Wu, Sha; Lu, Sheng; Xie, Xin; Deng, Shaoping; Jiang, Zixiao; Wang, Jian
• Is Part Of: IOP conference series. Volume 702:Issue 1(2021)
• Publication Date: 2021-03
• Subjects: Earth sciences -- Periodicals; Environmental sciences -- Congresses; Environmental sciences -- Periodicals; 550.5
• DOI: 10.1088/1755-1315/702/1/012018
• Languages: English
• ISSNs: 1755-1307

Abstract

Abstract: Compared with traditional wind turbine performance evaluation methods such as operating power curve fitting, the wind turbine performance model construction method using SCADA data on machine learning has gradually attracted attention in the industry in recent years. In this technical route, the choice of input SCADA data type will directly affect the accuracy of the wind turbine performance model construction. However, in the past, selecting key SCADA data types in the performance evaluation of wind turbines were mostly subjective judgments or correlation analyses. This paper proposed a key SCADA data type selection method based on mutual information calculation and a wind turbine performance model construction method based on deep neural network training. The selected key SCADA data types based on mutual information were applied to the deep neural network to construct the wind turbine performance model, and the actual SCADA operation data of the wind farm was applied to test the model. The results show that the model construction method's accuracy and generalizability using the above technical route can satisfy the industry demand in the same wind farm with the same wind turbine type.