Modelling and Analysis of Electric Machines

In this course, mathematical models of electrical machines are developed. The most common alternating current machines, synchronous and asynchronous machines, are treated thoroughly. The mathematical models are used in stationary and dynamic analysis of their behavior in power grids, industrial applications and motor drives. The models that are developed are based on classic 2-axis theory. Different variants of the models are developed and demonstrated for different areas of use. Simulation models are used for this purpose in in both lectures and exercises.The course further deals with the physical origin of the parameters included in the models. How these can be manipulated by design is dealt with to some extent. The course also describes factors that determine the load capacity of the machines.

Knowledge: After the course, the candidate should know the various static and dynamic models of the synchronous and asynchronous machine. The student must also be able to chosse a model with sufficient accuracy, depending on the application. The candidate must also know the machine's static and dynamic properties and behavior in various application. After completing the course, the candidate must know which physical parameters limit the capability of the machines. Skills: The candidate must be able to explain the various models and the most important dynamic phenomena in the machines that the models describe. The candidate must also be able to use these for various types of analyzes and in simulation in programs such as Python. The candidate should be able to analyze known dynamic phenomena in the event of short-circuiting, start-up and load changes. Furthermore, the student must be able to use the models for controlling the machines. General competence:

The teaching is given using lectures and exercises. The lecture contains small exercises that students solve either analytically or using Python. Results are discussed at commented in the class.

The exercises include programming using Python. The programmed machine models are then used in simulations. There are normally 5 larger exercises in the course semester. The students work in groups of 2.