Analysis and comparison of turbulence models on wind turbine performance using SCADA data and machine learning technique

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.

Keywords:

• wind turbine
• SCADA system
• wind turbulence
• machine learning
• wind turbine components

Public Interest Statement

The paper highlights the different turbulence models that can be used to monitor the behavior of the turbulence intensity of the wind turbine using SCADA data. Through this study, the SCADA data can be used as a reference for the generation and monitoring of turbulence using Machine Learning Techniques. Wind turbulence intensity, if properly monitored, could help in the improvement of the reliability of the wind turbines. Developing a data-driven turbulence model is a cost-effective and convenient method of modeling wind turbulence.

Acknowledgements

The support from Taipower Company was highly appreciated.

Author contributions

Conceptualization, Jui-Hung Liu, Jien-Chen Chen, and Nelson T. Corbita Jr.; Data curation, Jui-Hung Liu; Formal analysis, Jien-Chen Chen and Nelson T. Corbita Jr.; Investigation, Jui-Hung Liu; Methodology, Jui-Hung Liu and Nelson T. Corbita Jr.; Project administration, Jui-Hung Liu and Jien-Chen Chen; Validation, Nelson T. Corbita Jr.; Writing – original draft, Nelson T. Corbita Jr.; Writing – review & editing, Jui-Hung Liu. All authors have read and agreed to the published version of the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The authors received no direct funding for this research.

Notes on contributors

Jui-Hung Liu

Jui-Hung Liu and his team have been focused on large wind farm operations and maintenance for over 15 years. Based on Taiwan’s special wind conditions for the wind turbines, the team keeps developing monitoring systems, control strategies, and intelligent algorithms to improve the turbines’ operation and performance. The present paper utilizes the SCADA data and relates to the special turbulent condition in that wind farm to show the importance of a correct turbulent model.