Artificial-Intelligence-Based Reduced Sensor Voltage Control Strategy for DC Microgrid Applications
Approved
Classifications
MinEdu publication type
A1 Journal article (peer-reviewed)
Definition
Article
Target group
Scientific
Peer reviewed
Peer-reviewed
Article type
Journal article
Host publication type
Journal
Publication channel information
Title of journal/series
Iet renewable power generation
ISSN (print)
1752-1416
ISSN (electronic)
1752-1424
ISSN (linking)
1752-1416
Publisher
Institution of engineering and technology
Publication forum ID
57631
Publication forum level
2
Publication appears in FT-list
No
SNIP-level of the publication
0.95
Country of publication
United Kingdom
Internationality
Yes
Detailed publication information
Publication year
2025
Reporting year
2025
Journal/series volume number
19
Journal/series issue number
1
Article number
e70072
DOI
10.1049/rpg2.70072
Language of publication
English
Co-publication information
International co-publication
No
Co-publication with a company
No
Availability
Link to online publication
Link to self-archived version
Classification and additional information
MinEdu field of science classification
213 Electronic, automation and communications engineering, electronics
Keywords
AI; Microgrid; DC-DC Converter; Machine Learning; Reduced Sensor; Voltage Control; DC–DC power converters; Hardware‐in‐the loop simulation; Microgrids; Neural networks; Predictive control; Reduced sensor voltage control
Funding information
Funding information in the publication
This work is carried out in a project titled Smart Grid 2.0 with the financial support provided by Business Finland under Grant No. 1386/31/2022.
Funders
Funder
Business Finland
Name of funding
-
Funding decision
-
Research data information
Research data information in the publication
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Source database ID
WoS ID
WOS:001576579300001
Scopus ID
2-s2.0-105007745938