Analysis and comparison of turbulence models on wind turbine performance using SCADA data and machine learning technique. Issue 1 (31st December 2023)

• Record Type: Journal Article
• Authors: Liu, Jui-Hung; Chen, Jien-Chen; Corbita, Nelson T.
• Is Part Of: Cogent engineering. Volume 10:Issue 1(2023)
• Publication Date: 2023-12-31
• Subjects: wind turbine -- SCADA system -- wind turbulence -- machine learning -- wind turbine components; Engineering -- Periodicals; Technology -- Periodicals; Engineering; Technology; Periodicals; 620
• DOI: 10.1080/23311916.2023.2167345
• Languages: English
• ISSNs: 2331-1916

Abstract

Abstract: Wind energy has grown significantly over the last decade. With this, various improvements in the design of the wind turbine are geared towards increasing the reliability of several components. Wind turbulence has a huge effect on the fatigue loading of wind turbines considered in the design. Several monitoring methodologies, such as turbulence intensity analysis, are used to identify wind turbulence. In this paper, a method based on machine learning techniques and data from Supervisory Control and Data Acquisition (SCADA) systems is described. Five machine learning models are generated and compared in this study with the use of the operational data from the SCADA of wind turbines in a single wind farm. Results showed that the model based on Linear Regression in terms of a quadratic hyperparameter has lesser errors compared to the other models that were generated. Each parameter used in the creation of the model affects its performance. Observations in the nacelle system also showed higher errors due to the relationship between rotor speed and the blade angle. The rotor performance is mostly influenced by wind turbulence as the variation in wind speeds and rotational speeds have a certain correlation. Based on the results, it can be concluded that the use of SCADA data in generating turbulence models provides key insights into the relation of the turbulence intensity to the various components. It can be used as the basis for developing turbulence monitoring models that could help improve the design and operation of wind turbines.