Detection of mass imbalance in the rotor of wind turbines using Support Vector Machine.

Condition monitoring systems (CMS) are essential to reduce costs in the wind energy sector. This paper proposes a method based on Support Vector Machine (SVM) to detect rotor mass imbalance for a multi-class imbalance problem, using the estimated speed as an input variable, obtained through a combination of electrical quantities (currents and voltages). Moreover, it is sought to obtain the magnitude of the rotor mass imbalance. With the aid of statistical tools, intermediate classes can be estimated, other than the ones proposed for the SVM. Besides, if the azimuth position is provided, the angular position of the mass imbalance can be also obtained. A 1.5 MW three-bladed wind turbine model with a permanent magnet synchronous generator, was considered, and a database was built numerically using the software Turbsim, FAST, and Simulink. From the database, the Power Spectral Density (PSD) technique was used to transform the input data from the time to the frequency domain. Then, the SVM algorithm and statistical analysis were used to classify the magnitude and the angular position of the imbalance. Different scenarios of mass imbalance were tested under different wind speeds and turbulence intensities. The results demonstrate the satisfactory performance of the proposed method. [Display omitted] • This paper proposes a method to detect rotor mass imbalance in wind turbines. • Support Vector Machine (SVM) is used to classify imbalance from electrical signals. • The method locates the equivalent angular position of the rotor mass imbalance. • The method estimates intermediate imbalance classes from the SVM classes. • The impact of the wind turbulence intensity on the SVM performance was investigated. [ABSTRACT FROM AUTHOR]