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. ? 2021 Elsevier Ltd. All reserved.

Automated summary

Condition monitoring systems are crucial for cost reduction in the wind energy sector. This paper proposes a method based on Support Vector Machine to detect rotor mass imbalance, using estimated speed and electrical quantities as input variables, and aiming to obtain the magnitude and angular position of the imbalance. Statistical tools are employed to estimate intermediate classes, improving detection accuracy.