34642 - Electromechanical systems modelling M

Academic Year 2017/2018

  • Docente: Angelo Tani
  • Credits: 6
  • SSD: ING-IND/32
  • Language: Italian
  • Teaching Mode: Traditional lectures
  • Campus: Bologna
  • Corso: Second cycle degree programme (LM) in Electrical Energy Engineering (cod. 8611)

Learning outcomes

The aim of the course is to provide instruments useful in order to define mathematical models suitable for studying, designing and controlling electromechanical systems.

Course contents

Electromechanical systems dynamics

Introduction to mathematical modelling of electromechanical systems, Input-Output and Input-State-Output differential equations, stability analysis, small signal analysis, numerical solution of differential equations. Analysis of electrodynamic and electromagnetic levitation systems.

 

Space vectors in three-phase systems

Definition of space vector and zero sequence component, differential equations of three-phase systems in terms of space vectors, Fourier expansion of space vectors, mathematical representation of quantities depending on space and time, multiple space vectors for multiphase systems.

 

Analysis of electric machines by space vectors

Modelling principles for rotating electrical machines, dynamic model of the induction machine in terms of space vectors and zero-sequence components, machine parameter estimation, direct torque and flux control (DTC) of induction machines.

 

Discrete-time systems

Introduction to mathematical modelling of discrete-time systems, Input-Output and Input-State-Output discrete equations, stability analysis, discretization of the differential equations of continuous-time systems.

 

Parameters and state estimation of an electromechanical system

On line parameters estimation of an electromechanical system by MRAS method, full order state observer, adaptive state observer.

 

Fuzzy controllers

Introduction to fuzzy logic, linguistic variables, fuzzy sets, membership functions, inference process, fuzzy logic for electromechanical system modelling and control.

 

Artificial neural network

Artificial neurons and activation functions, multilayer neural networks, learning process by back propagation algorithm, neural networks for electromechanical system modelling and control.

 

The lessons are supported by exercises with Personal Computer.

 

Readings/Bibliography

The pdf files of the slides utilized during the lessons can be downloaded from Internet.

Teaching methods

The lessons are supported by exercises with Personal Computer, involving the analysed mathematical models.

Assessment methods

The assessment of learning is based on an oral examination.

Teaching tools

Lessons and exercises are carried out with the help of a personal computer and a computer projector (Power Point, MATLAB).

Office hours

See the website of Angelo Tani