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Events at Department of Energy Technology

CANCELLED: Industrial/PhD Course: Artificial Intelligence in Electrical Energy Systems

Time

24.09.2020 kl. 08.30 - 25.09.2020 kl. 16.00

Description

Organizer

Prof. Tomislav Dragičević

Lecturers

Prof. Tomislav Dragičević,
Dr. Mateja Novak

ECTS: 2

Description

Artificial intelligence (AI) has recently become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. On the other hand, AI has also been used in Electrical Engineering for many decades for data-driven modelling of system whose analytic modelling was hard or simply not possible to do. For instance, they have been widely used for load/generation/price forecasting, for modelling nonlinear parts of the industrial control systems and for creating surrogate models of complex systems. All these applications are enabled by the artificial neural networks – the fundamental workhorses of the AI.

However, as opposed to past decades when neural networks were small and comprised only a few neurons, recent dawn of the big data age (characterized by the unprecedented access to large computational resources and big datasets) has enabled the creation of much larger networks. They have greatly advanced the computer vision field but have recently also enabled many new applications in the electrical engineering field.

In a nutshell, this course is focused on providing the attendees the following material: a) AI historical background in electrical engineering and wider, b) an understanding and fundamental characteristics of artificial neural networks, and c) practical applications of AI proposed by the lecturers that have solved some of the long standing research problems in electrical engineering. All models will be provided to attendees and experimental lab demonstration is expected as well.

Upon completing the course, the participants will gain insight how to obtain a high accuracy surrogate model of a power electronics systems, utilize the surrogate for multi-objective optimization problems or synthesis of complex power electronics controllers. 

Day 1: General information about AI, Tomislav Dragičević (4.5 hours) + Mateja Novak (2.5 hours)

9:00 – 10:00 Artificial intelligence – how is it revolutionizing the world

10:00 – 10:30 Features of neural networks – the workhorses of AI

10:30 – 11:00 Coffee break

11:00 – 12:00 Artificial intelligence in electrical engineering – historical background

12:00 – 13:00 Lunch break

13:00 – 14:00 Application of AI to optimize design of power electronic systems

14:00 – 15:00 Imitation learning of computationally heavy controllers

15:00 – 15:30 Coffee break

15:30 – 16:30 AI for optimizing the control parameters of industrial control systems

Day 2: AI laboratory exercises. Tomislav Dragičević (3.5 hours) + Mateja Novak (3.5 hours)

08:30 – 10:30 Laboratory 1: AI-aided design for reliability of power electronics systems

10:30 – 11:00 Coffee break

11:00 – 12:00 Laboratory 2: AI-aided imitation learning of industrial control systems (part 1)

12:00 – 13:00 Lunch break

13:00 – 14:00 Laboratory 2: AI-aided imitation learning of industrial control systems (part 2)

14:00 – 15:00 Laboratory 3: AI-aided tuning of control parameters (part 1)

15:00 – 15:30 Coffee break

15:30 – 16:30 Laboratory 3: AI-aided tuning of control parameters (part 2)

Prerequisites

General knowledge about electrical engineering field.

Practicing knowledge in power electronic systems.

Experience in using Matlab/Simulink (Deep Learning toolbox, Parallel Computing toolbox)

The course is recommended for PhD students and power electronic control engineers focusing on multi-objective optimization problems and implementation of complex control algorithms

Form of evaluation

Report evaluated by the lecturers.

Registration

https://phd.moodle.aau.dk

Price

6000 DKK for PhD students outside of Denmark and 8000 DKK for the Industry.

Host

Department of Energy Technology

Address

Aalborg University, Pontoppidanstræde 101, room 1.015, 9220 Aalborg East