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Artificial Intelligence Aided Predictive Control of Power Electronic Converters for Distributed Generation Systems, Electric Drivers and Microgrids
Gómez, Pere Izquierdo
(PhD Student)
Dragicevic, Tomislav
(Main Supervisor)
Mijatovic, Nenad
(Supervisor)
Wang, Huai
(Examiner)
Yang, Tao
(Examiner)
Power and Energy Systems
E-mobility and Prosumer Integration
Department of Wind and Energy Systems
PowerLabDK
Overview
Fingerprint
Publications
(1)
Project Details
Status
Finished
Effective start/end date
01/10/2020
→
14/08/2024
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Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.
Machine Learning
Computer Science
100%
Electronic System
Computer Science
100%
Power Electronics
Engineering
100%
Surrogate Model
Engineering
66%
Computing Platform
Computer Science
50%
Training Process
Computer Science
50%
Control Algorithm
Computer Science
50%
Machine Learning Method
Engineering
33%
Research output
Research output per year
2023
2023
2023
1
Ph.D. thesis
Research output per year
Research output per year
Resource-efficient Machine Learning for Power Electronic Systems
Gómez, P. I.
,
2023
, Risø, Roskilde, Denmark:
DTU Wind and Energy Systems
.
164 p.
Research output
:
Book/Report
›
Ph.D. thesis
Open Access
File
Machine Learning
100%
Electronic System
100%
Power Electronics
100%
Resource-efficient Machine Learning
100%
Surrogate Modeling
100%
199
Downloads (Orbit)