Adaptive data-driven fault-tolerant control strategy for optimal power extraction in presence of broken rotor bars in wind turbine.

Broken rotor bars of a Squirrel Cage Induction Generator (SCIG) impact significantly the quality and quantity of produced electrical energy from Wind Energy Conversion Systems (WECS) because: (1) they change the characteristics of SCIG entailing the invalidity of the designed Maximum Power Point Trucking (MPPT) control and pitch angle control, (2) they increase mechanical and thermal stresses, as well as the harmonic content in stator currents. Therefore, this paper proposes an efficient fault tolerant control strategy in order to mitigate the aforementioned consequences. This strategy is based on a hybrid (mechanic-electric) fault tolerant control (FTC) allowing obtaining optimal power extraction in presence of broken rotor bars. A failure (one or two broken bars) is detected in early stage based on the use of a data-driven (machine learning) approach. The latter uses a discriminative feature space (frequency and magnitude of stator harmonic currents) in order to represent and separate the normal operation mode from faulty modes (broken bars). Simulations using MATLAB demonstrated a nominal current in healthy rotor bars in wind turbine operation zone-3 and new optimal power extraction despite one or two broken bars.