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• W2194328158 abstract "Fast and accurate fault diagnosis of the position sensor is of great significance to ensure the reliability as well as sensor fault tolerant operation of the Switched Reluctance Wind Generator (SRWG). This paper presents a fault diagnostic scheme for a SRWG based on the residual between the estimated rotor position and the actual output of the position sensor. Extreme Learning Machine (ELM), which could build a nonlinear mapping among flux linkage, current and rotor position, is utilized to design an assembled estimator for the rotor position detection. The data for building the ELM based assembled position estimator is derived from the magnetization curves which are obtained from Finite Element Analysis (FEA) of an SRWG with the structure of 8 stator poles and 6 rotor poles. The effectiveness and accuracy of the proposed fault diagnosis method are verified by simulation at various operating conditions. The results provide a feasible theoretical and technical basis for the effective condition monitoring and predictive maintenance of SRWG." @default.
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• W2194328158 date "2016-01-01" @default.
• W2194328158 modified "2023-10-18" @default.
• W2194328158 title "A Fault Diagnostic Method for Position Sensor of Switched Reluctance Wind Generator" @default.
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• W2194328158 doi "https://doi.org/10.5370/jeet.2016.11.1.029" @default.
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