A novel scheme for current sensor faults diagnosis in the stator of a DFIG described by a T-S fuzzy model

• Nonlinear state space model is developed for the DFIG-based WT.
• A new T-S fuzzy model is proposed for the DFIG in the operating Region (2) of WT.
• A novel FDI scheme is designed, which exhibits single as well as multiple and simultaneous CSF detection and isolation.
• It is simple and can be easily implemented in real time.
• It will be benefit for practical application in power energy conversion systems.

Diagnosis of current sensor faults (CSF) for doubly fed induction generators (DFIGs) is of paramount importance for the reliable power generation of DFIG-based wind turbines (WT). In this paper, a new scheme is developed for current sensors faults diagnosis in the stator of a DFIG-based WT. The nonlinear model of the DFIG is first transformed into an equivalent Takagi-Sugeno (T-S) fuzzy model. Secondly, using this model, a novel fault detection and isolation (FDI) algorithm is proposed. This algorithm is based on a bank of Luenberger observers for residuals generation combined with a new proposed residual vector. Furthermore, a new binary decision logic is used for CSF isolation. Stability analysis of the observer bank is analyzed using a Lyapunov theorem, which allows deriving sufficient stability conditions by solving a system of Linear Matrix Inequalities (LMIs). A simulation study is carried out to assess the performance and the effectiveness of the new FDI scheme.

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