This paper presents a data-driven approach to short-term wind turbine fault prediction and condition monitoring based on a hybrid architecture of recurrent neural network (RNN) and long short-term memory (LSTM). The proposed architecture is established by utilizing time series data from SCADA and a Bladed model of a 5 MW wind turbine to predict faults occurring to the wind generator. The RNN-LSTM training procedure is enhanced with self-organizing maps and LSTM auto encoder so as to describe the complex interaction between the mechanical system and unpredictable wind speed. To verify the performance of the proposed scheme, we conduct in-depth numerical experiments by applying the hybrid architecture to the Bladed 5 MW wind turbine model with rated wind speed of 11.8 m/s. Experiment results confirm that the proposed scheme has superior accuracy and practicality of fault prediction compared with eminent existing machine learning algorithms such as extreme gradient boost and random forest regressor.

Wind turbines are subjected to asymmetric loads and fatigue with subsequent increases in their dimension and capacity, leading to a reduction in their lifetime. To address this problem, the individual pitch control (IPC) technique is quite familiar in the control of wind turbines. IPC is used to reduce the tilt and yaw moments, simultaneously alleviating the turbine blade-root bending moments (BRBMs). This study discusses the performance of model predictive control (MPC), H-infinity ($H_{\infty }$), and proportional and integral (PI)-based IPC strategies integrated with collective pitch control. The performance of the reported controllers has been validated using the National Renewable Energy Laboratory (NREL) 5 MW full nonlinear reference wind turbine. Simulation studies are conducted at varying wind speeds and turbulent intensities as per international electrotechnical commission (IEC) norms. Comparative results in the time and frequency domains indicate that the $H_{\infty }$ based IPC achieves enhanced control performance in terms of reduction in BRBMs and damage equivalent load compared to MPC and PI-based control strategies.