# 34642 - Electromechanical systems modelling M

• Docente: Angelo Tani
• Credits: 6
• SSD: ING-IND/32
• Language: Italian
• Campus: Bologna
• Corso: Second cycle degree programme (LM) in Electrical Energy Engineering (cod. 9066)
• from Sep 18, 2023 to Dec 18, 2023

## 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

Prerequisites

Basic skills in electrical engineering.

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.

It is not necessary to buy specific books. The pdf files of the slides utilized during the lessons are indispensable and sufficient for the preparation for the exam, and are available on VIRTUALE.

## Teaching methods

The frontal lessons are supported by exercises with Personal Computer (MATLAB-Simulink).

## Assessment methods

The exam consists of an oral examination, which is based on three questions.

## Teaching tools

Lessons are carried out with the help of a personal computer and a computer projector (Power Point). The pdf files of the slides utilized during the lessons are available on VIRTUALE. Exercises are carried out with the help of a personal computer (MATLAB-Simulink).

## Office hours

See the website of Angelo Tani

### SDGs

This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.