This thesis adresses the problem of estimating the parameters in a permanent magnet synchronous motor (PMSM). There is an uncertainty about the parameters, due to age and tolerances in the manufacturing process. Parameters such as the resistance and the current to torque factor Kt, changes with respect to temperature as well. The temperature in the motor varies in normal motor operation, due to variations in angular velocity and torques. Online estimation methods with the model reference adaptive systems technique (MRAS) and offline methods are presented. The estimation algorithms are validated in simulations with Matlab/Simulink and also evaluated with experimental data. Experiments were performed on a range of different motors, in realistic scenarios. Relevant factors such as the angular velocity of the rotor and the impact of the gravity force are investigated. The results show that it is possible to estimate the motor factor $K_t$, with an accuracy of two percentage from its reference value in normal industry conditions. The estimated value of the motor inductance is within 25 percentage of the calculated reference value. The resistance however is affected by the resistance in the cables from the motor to the measurement device. With the cable resistance included in the calculations, the estimate still often exceeds double the value of the reference value.

In industrial drives market, requirements related to control quality and price of drives are important. In low cost drives, one of the aims is achieving speed estimation accuracy. Since motor parameters are required to estimate speed and sometimes it is impractical to do no-load and locked rotor tests, it is necessary to estimate motor parameters from motor label or by simple measurements. Throughout this study, some of parameter estimation and speed estimation methods found in literature are investigated and some new methods are proposed. These methods are applied to three induction motors and estimation results are compared with test results. Advantages and disadvantages of these methods are investigated. As a result of this study, the most suitable parameter and speed estimation methods amongst these methods are obtained for low cost motor drives.

The organic materials used for stator winding insulation are subject to deterioration from thermal, electrical, and mechanical stresses. Stator winding insulation breakdown due to excessive thermal stress is one of the major causes of electric machine failures; therefore, prevention of such a failure is crucial for increasing machine reliability and minimizing financial loss due to motor failure. This work focuses on the development of an efficient and reliable stator winding temperature estimation scheme for small to medium size mains-fed induction machines. The motivation for the stator winding temperature estimation is to develop a sensorless temperature monitoring scheme and provide an accurate temperature estimate that is capable of responding to the changes in the motors cooling capability. A discussion on the two major types of temperature estimation techniques, thermal model-based and parameter-based temperature techniques, reveals that neither method can protect motors without sacrificing the estimation accuracy or motor performance. Based on the evaluation of the advantages and disadvantages of these two types of temperature estimation techniques, a new online stator winding temperature estimation scheme for small to medium size mains-fed induction machines is proposed in this work. The new stator winding temperature estimation scheme is based on a hybrid thermal model. By correlating the rotor temperature with the stator temperature, the hybrid thermal model unifies the thermal model-based and the parameter-based temperature estimation techniques. Experimental results validate the proposed scheme for stator winding temperature monitoring. The entire algorithm is fast, efficient and reliable, making it suitable for implementation in real time stator winding temperature monitoring.