Abstract
A novel concept of wind farm fault detection by monitoring the wind speed in the wake region is proposed in this study. A wind energy dissipation model was coupled with a computational fluid dynamics solver to simulate the fluid field of a wind turbine array, and the wind velocity and direction in the simulation were exported for identifying wind turbine faults. The 3D steady Navier–Stokes equations were solved by using the cell center finite volume method with a second order upwind scheme and a k−ε turbulence model. In addition, the wind energy dissipation model, derived from energy balance and Betz’s law, was added to the Navier–Stokes equations’ source term. The simulation results indicate that the wind speed distribution in the wake region contains significant information regarding multiple wind turbine faults. A feature selection algorithm specifically designed for the analysis of wind flow was proposed to reduce the number of features. This algorithm proved to have better performance than fuzzy entropy measures and recursive feature elimination methods under a limited number of features. As a result, faults in the wind turbine array could be detected and identified by machine learning algorithms.
Highlights
Wind energy is one of the renewable and non-polluting energy sources
Simulation results of wind speeds presented in the previous section were used to generate the training and testing data for the condition monitoring of the wind turbines
Twelve features including root mean square (RMS), Standard Deviation (STD), variance, and Kurtosis of the wind speed in the x, y, and z directions were extracted from the original dataset
Summary
Wind energy is one of the renewable and non-polluting energy sources. It has become widely popular and is expected to replace conventional energy sources, such as nuclear energy and coal. A novel feature selection algorithm based on the distance between feature distributions was proposed for extracting significant information from the wind speed in the wake region and improving the classification accuracy Machine learning methods such as artificial neural network (ANN), k-nearest neighbors (KNN), support vector machine (SVM), and similarity classifier (SC) were applied to identify wind turbine status. Those machine learning techniques are advantageous for condition monitoring because of their ability to represent complex and nonlinear relationships such as wind speed in the wake region through a learned pattern recognition
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
