Application of Probabilistic Neural Network in Fault Diagnosis of Wind Turbine Using FAST, TurbSim and Simulink

This paper presents an intelligent diagnosis technique for wind turbine imbalance fault identification based on generator current signals. For this aim, Probabilistic Neural Network (PNN), which is powerful algorithm for classification problems that needs small training time in solving nonlinear problems and applicable to high dimension applications, is employed. The complete dynamics of a permanent magnet synchronous generator (PMSG) based wind-turbine (WTG) model are imitated in an amalgamated domain of Simulink, FAST and TurbSim under six distinct conditions, i.e., aerodynamic asymmetry, rotor furl imbalance, tail furl imbalance, blade imbalance, nacelle-yaw imbalance and normal operating scenarios. The simulation results in time domain of the PMSG stator current are decomposed into the Intrinsic Mode Frequency (IMF) using EMD method, which are utilized as input variable in PNN. The analyzed results proclaim the effectiveness of the proposed approach to identify the healthy condition from imbalance faults in WTG. The presented work renders initial results that are helpful for online condition monitoring and health assessment of WTG.