Data Driven Fault Detection and Isolation of a Wind Turbine Benchmark

This paper investigates data-driven fault detection and isolation (FDI) designs for a wind turbine benchmark problem. The benchmark is described by a SimuLink model, which contains nonlinear lookup tables and unknown wind disturbances. Based on classical FDI design methods, a linearization of the SimuLink model into a standard state-space form and describing the linearization errors as perturbations may be necessary. To avoid these difficult modeling procedures, this paper applies a data-driven design method, which produces FDI filters directly based on the simulated data from the benchmark SimuLink model. The fixed-value sensor faults therein are especially targeted. Moreover, we develop in this paper a new data-driven fault isolation scheme, via exploiting hardware redundancy in a plant. Based on this, a bank of robust data-driven detection filters are designed for the benchmark and implemented in parallel. The simulation results show the effectiveness of the applied data-driven scheme.