Artificial neural networks for control of a grid-Connected rectifier/inverter under disturbance, dynamic and power converter switching conditions
- Submitting institution
-
City, University of London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 738
- Type
- D - Journal article
- DOI
-
10.1109/TNNLS.2013.2280906
- Title of journal
- IEEE Transactions on Neural Networks and Learning Systems
- Article number
- -
- First page
- 738
- Volume
- 25
- Issue
- 4
- ISSN
- 2162-237X
- Open access status
- Out of scope for open access requirements
- Month of publication
- April
- Year of publication
- 2014
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
5
- Research group(s)
-
-
- Citation count
- 54
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This output proposes an original solution to the problems experienced with industrial control mechanisms in terms of stability under disturbance for ubiquitous three-phase grid-connected converters used in renewable and electric power system applications. With this technology, we were awarded the First Prize of the of the European Institute of Innovation and Technology (EIT) ICT Labs Idea Challenge on Smart Energy Systems in Berlin October 2014, the first time a UK-based team won such a competition.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
