This paper presents a new approach to detect and isolate the current sensor faults, a doubly fed induction generator (DFIG) for a wind turbine application. And to detect the variable resistance faults. A method using an unknown input of multiple observers described via Takagi-Sugeno (T-S) multiple models. A bank of multiple observers scheme (DOS) generates a set of residuals for detection and isolation of sensor faults which can affect a TS model. A decision system is used to the process the residual vector to detection and isolation faults. The stability and the performance of the multiple models are formulated in terms of Linear Matrix Inequalities (LMIs). The approach is validated using Matlab software to modeling and simulation of a DFIG. Recently several researchers have explored a Takagi-Sugeno (TS) fuzzy observer to deal with nonlinearity in problems detection and diagnosis. A considerable research proposed linear parameter-varying FTC systems for pitch actuator faults occurring in the full load operation. A T-S fuzzy observer based FTC design is proposed to achieve maximization of the power extraction. Other examples of this usage of the unknown input observers is use where the former reports similar schemes applied on fault detection of power plant coal mills and the latter estimate power coefficients for wind turbines, some examples can be found of fault detection and accommodation of wind turbines. An observer based scheme for detection of sensor faults for blade root torque sensor is presented in other research. The unknown input observer based scheme was proposed to detect such faults in a wind turbine. The contribution of this paper focuses on the design of the unkown multiple observers to detect, isolate the current sensor faults and fault of variation resistance in the rotor circuit in DFIG, based on the wind turbine T-S models. Keyword: Wind Turbine; Current sensor Fault; parameter variation; Takagi-Sugeno Multiple models; Multiple Observer