This paper presents a new technique for fault detection and isolation to make the traditional vector-controlled induction motor (IM) drive fault tolerant against current and speed sensor failure. The proposed current estimation uses d- and q-axes currents and is independent of the switching states of the three-leg inverter. While the technique introduces a new concept of vector rotation to generate potential estimates of the currents, speed is estimated by one of the available model reference adaptive system (MRAS) based formulations. A logic-based decision mechanism selects the right estimate and reconfigures the system (by rejecting the signal from the faulty sensors). Such algorithm is suitable for different drives, including electric vehicles to avoid complete shutdown of the system, in case of sensor failure. The proposed method is extensively simulated in MATLAB/SIMULINK and experimentally validated through a dSPACE-1104-based laboratory prototype.

An artificial neural network (ANN) based adaptive estimator is presented in this paper for the estimation of rotor speed in a sensorless vector-controlled induction motor (IM) drive. The model reference adaptive system (MRAS) is formed with instantaneous and steady state reactive power. Selection of reactive power as the functional candidate in MRAS automatically makes the system immune to the variation of stator resistance. Such adaptive system performs satisfactorily at very low speed. However, it is observed that an unstable region exists in the speed-torque domain during regeneration. In this work, ANN is applied to overcome such stability related problem. The proposed method is validated through computer simulation using MATLAB/SIMULINK. Sample results from a laboratory prototype (using dSPACE-1104) have confirmed the usefulness of the proposed estimator.

Various functional candidates such as flux, back electromotive force, and reactive power are used to form a model reference adaptive system (MRAS) to estimate the speed of an induction motor drive. Of these, reactive power (Q) based controllers perform well at low speeds and are inherently independent of stator resistance. However, such configuration fails to provide stability in the regenerative mode. This paper proposes a new formulation of reactive-power-based MRAS that is stable in all the four quadrants of operation. A detailed MATLAB/Simulink based simulation study is presented. Superior performance of the proposed controller is confirmed by prototype experimentation using a field-programmable-gate-array based controller in the laboratory.

New speed and current estimation techniques are proposed in this paper. These are used to formulate a single current sensor-based vector controlled drive. The speed sensorless control is formulated by developing a new model reference adaptive system (MRAS) using the outer product of →vs^* and →is as the functional candidate. Contrary to the method available that depends on stator resistance, the proposed approach is immune to the variation of stator resistance. This is possible by using reference quantities (i.e., voltage and currents) in reference model and actual quantities (i.e., currents) in adjustable model. The current estimation on the other side extracts the information of the phase currents in two-phase stationary reference frame by using the corresponding synchronous reference frame variables and the vector rotator. The current estimation also does not depend on stator resistance and hence the complete system can perform very well at low speed. The proposed speed and current estimation techniques are simulated in MATLAB/Simulink and experimentally validated through a dSPACE-1104-based laboratory prototype.

This paper presents new stator resistance ( ${R_{s}}$ ) estimation techniques for a vector controlled Permanent Magnet Synchronous Motor (PMSM) drive. These techniques give the indirect measurement of the temperature rise in the stator. An accurate estimation of the ${R_{s}}$ will help in condition-monitoring of the PMSM machine. The various ${R_{s}}$ estimation techniques proposed in this paper are formulated using Model Reference Adaptive System (MRAS). Out of the various methods proposed, p-MRAS performs better than the remaining stator resistance estimation techniques. since this independent of integrator, differentiator, and speed; offers robust, and dynamic performance in all four modes of operation including zero speed. Stability and Sensitivity analysis are further carried out to ensure the robustness of the proposed estimation techniques. The proposed stator resistance estimation techniques are simulated in MATLAB/Simulink and experimentally validated through a dSPACE-1104-based laboratory prototype.

A unique formation of model reference adaptive reference (MRAS) using, active power (P), reactive power (Q) and fictitious quantities (X and Y) as the functional candidates is presented. The formulation of MRAS in the proposed form has the advantage that, no machine parameters are involved, except the rotor resistance. The reference and adjustable model of modified MRAS are computed in rotating reference frame, using reference value of voltages and currents in the reference model, whereas, reference voltages and actual currents in the adjustable model. Stability analysis of the modified P, Q, X and Y based MRAS is performed in all the four quadrants of speed-torque domain. It is found that P and Y MRAS have unstable points, whereas, Q and X MRAS are stable in all the four quadrant of operation. The algorithm can be implemented without the need of any extra hardware, thus making it suitable for retrofit applications. The proposed MRAS based speed sensorless vector controlled IM drive have been simulated in MATLAB/SIMULINK and is experimentally validated with the laboratory developed prototype. The prototype is built around a dSPACE 1104 controller board.