Mikaela Ranta

Loss-Minimizing Flux Level Control of Induction Motor Drives

This paper applies a dynamic space-vector model to loss-minimizing control in induction motor drives. The induction motor model, which takes hysteresis losses and eddy-current losses as well as the magnetic saturation into account, improves the flux estimation and rotor-flux-oriented control. Based on the corresponding steady-state loss function, a method is proposed for solving the loss-minimizing flux reference at each sampling period. A flux controller augmented with a voltage feedback algorithm is applied for improving the dynamic operation and field weakening. Both the steady-state and dynamic performance of the proposed method is investigated using laboratory experiments with a 2.2-kW induction motor drive. The method improves the accuracy of the loss minimization and torque production, it does not require excessive computational resources, and it shows fast convergence to the optimum flux level.

Online Identification of Parameters Defining the Saturation Characteristics of Induction Machines

The induction machine model parameters need to be estimated with good accuracy to ensure a good performance of the drive. Due to the magnetic saturation, the inductances vary as a function of the flux level. The magnetizing curve can be identified at standstill, but more accurate results are obtained if the identification is performed as the machine is running. In this paper, the magnetic saturation is modeled using a power function, and adaptation laws for the function parameters are proposed. The adaptation method is implemented in the control system of a sensorless drive. Experimental results on a 2.2-kW machine show that the identification of the stator inductance is rapid and the accuracy is good.

Inclusion of hysteresis and eddy current losses in dynamic induction machine models

This paper proposes a method for including both hysteresis losses and eddy current losses in the dynamic space vector model of induction machines. The losses caused by the rotation and magnitude changes of the flux vector are taken into account. The model can be applied, for example, to time-domain simulations and real-time applications such as drive control. Finite element analysis, simulations, and laboratory experiments of a 45-kW motor are used for the investigation. It is shown that the model can predict the iron losses in a wide frequency range. The accuracy is significantly improved as compared to earlier models.

Loss-minimizing flux level control of induction motor drives

The paper applies a dynamic space-vector model to loss-minimizing control in induction motor drives. The induction motor model, which takes hysteresis losses and eddy-current losses as well as the magnetic saturation into account, improves the flux estimation and rotor-flux-oriented control. Based on the corresponding steady-state loss function, a method is proposed for solving the loss-minimizing flux reference at each sampling period. A flux controller augmented with a voltage feedback algorithm is applied for improving the dynamic operation and field weakening. Both the steady-state and dynamic performance of the proposed method is investigated using laboratory experiments with a 2.2-kW induction motor drive. The method improves the accuracy of the loss minimization and torque production, it does not require excessive computational resources, and it shows fast convergence to the optimum flux level.

Small-Signal Modeling of Mutual Saturation in Induction Machines

A small-signal model is derived for saturated induction machines. Inductances are allowed to saturate as a function of their own current (or flux), and the mutual saturation effect originating mainly from skewed or closed rotor slots is also included in the model. The model fulfills the reciprocity conditions, and it can be applied to parameter identification and to the analysis and development of flux-angle estimation methods. As application examples, the parameters of a 2.2-kW induction machine were identified using the data obtained from time-stepping finite-element analysis and locked-rotor measurements. The proposed model fits well to the data, and the fitted parameters are physically reasonable.

Inclusion of hysteresis and eddy current losses in nonlinear time-domain inductance models

A time-domain model including the core losses of a nonlinear inductor is proposed. The model can be seen as a parallel combination of a nonlinear inductance modelling the saturation and a nonlinear resistance modelling the core losses. The desired steady-state core-loss profile is used to determine the resistance function. The model is easy to implement and can be used in many different applications. The hysteresis loop of an electrical steel sample is measured at several frequencies in order to experimentally validate the model. It is shown that the model is able to predict both major and minor hysteresis loops very well.

Inductance identification of an induction machine taking load-dependent saturation into account

The paper proposes an identification method for the inductances of induction machines, based on signal injection. Due to magnetic saturation, a saturation-induced saliency appears in the induction motor, and the total leakage inductance estimate depends on the angle of the excitation signal. The proposed identification method is based on a small-signal model that includes the saturation-induced saliency. Because of the saturation, the load also affects the estimate, and measurements are needed in different operating points. Using the identified total leakage inductance, an estimate of the stator inductance can be obtained. The identification method is applied to computer simulations and laboratory experiments of a 2.2-kW induction motor.

Rotor parameter identification of saturated induction machines

An induction machine model is proposed for the identification of rotor parameters using high-frequency signal injection. The model includes both the magnetic saturation caused by the fundamental-wave components and the frequency dependence encountered in the signal injection method. Both the skin effect in the rotor winding and the eddy current losses in the rotor core are taken into account. Sinusoidal signal injection is used at several frequencies, and the model parameters are fitted to the results. The rotor leakage inductance and the rotor resistance valid at low slip frequencies are also obtained from the model directly. Experimental results for a 45-kW machine are presented. It is shown that the model fits well to the measured data in various operating points, and the accuracy of the identified parameters is good.