Internal leakage detection for wind turbine hydraulic pitching system with computationally efficient adaptive asymmetric SVM

This paper investigate an asymmetric support vector machine approach for wind turbine hydraulic pitch systems. Hydraulic pitching system in the wind turbine is critical for energy capture, load reduction and aerodynamic braking. Its reliability and maintenance is thus of high priority. The fault of cylinder internal leakage is studied in this paper. The fault and not-fault conditions for the internal leakage in the hydraulic system are classified through the self-learning asymmetric support vector machine (ASVM) algorithm, which can maintain the complexity of the fault model. The improved ASVM algorithm can adaptively select the minimal number of support vectors while maintaining the desired classification performance, which makes the practical implementation of the classifier computationally more efficient. The proposed method is verified through the simulation study based on the aerodynamic loading on the pitching axis under smooth and turbulent wind profiles obtained from the simulation of a 1.5 MW variable-speed turbine model on the FAST (Fatigue, Aerodynamics, Structural and Tower) software developed by the National Renewable Energy Laboratory (NREL).