This work proposes a novel real-time detection scheme for incipient stator inter-turn short circuit fault in voltage sources inverter-fed induction machines. Both non-sinusoidal input voltage and the short circuit fault causes harmonics in the motor stator current and these combined harmonic components complicate the spectral analysis-based diagnosis in inverter-fed motors. Aim of the analysis is to identify the effect of inverter fundamental/switching frequency on early detection and classification of the inter-turn fault. Discrete wavelet transform based analysis is performed on stator current using daubechies1 wavelet and statistical parameter L2 norm has been computed for the detailed and approximate coefficients at different decomposition levels to obtain the most precise feature of fault. Support vector machine-based learning algorithm is used for the accurate classification of the incipient fault. The proposed method is independent of switching and fundamental frequency, the modulation index and mechanical load. Real-time detection is possible even with infinitesimal fault current of 350 mA by the proposed method. The competency of the proposed algorithm is validated using simulation and verified by hardware with VSIfed induction motor drive.
Torque estimation for Direct Torque Controlled BLDC motor drive using Kalman Filter and Extended Kalman Filter are described in this paper. In conventional direct torque control method, the torque estimation is done based on the position information, which in turn requires position sensors for accurate estimation. In this paper, torque is directly estimated using Kalman filter and Extended Kalman Filter algorithms without the aid of any feedback information, and a comparison is made between the two. Both the algorithms are perceptive to follow the actual torque; the error between the actual and reference is used for the selection of appropriate voltage vector for inverter switching. The reflection of load changes in the control algorithm is also a part of the interest in this work. The potential of Extended Kalman Filter to follow exactly the changes in the load torque is utilized here and Extended Kalman Filter based sensorless drive is proposed for the torque estimation in Direct Torque Controlled BLDC motor. The performance parameters, viz. computational effort, torque ripple reduction are found to be superior with the Extended Kalman Filter algorithm, which is validated.
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