A Novel Adaptive Neural Network Constrained Control for a Multi-Area Interconnected Power System With Hybrid Energy Storage
- Submitting institution
-
The University of Kent
- Unit of assessment
- 12 - Engineering
- Output identifier
- 10054
- Type
- D - Journal article
- DOI
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10.1109/TIE.2017.2767544
- Title of journal
- IEEE Transactions on Industrial Electronics
- Article number
- -
- First page
- 6625
- Volume
- 65
- Issue
- 8
- ISSN
- 0278-0046
- Open access status
- Compliant
- Month of publication
- October
- Year of publication
- 2017
- URL
-
https://kar.kent.ac.uk/64199/
- 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
-
3
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper considers control of hybrid energy storage systems (HESS) to obtain optimized operation for load-frequency control. It is significant because a rigorous but practical feedforward neural network based control scheme is proposed for the HESS, which is adaptive and intelligent with higher performance. Simultaneously a novel dynamic anti-windup signal is designed to solve the operational constraints. This paper is the first development of control system for interconnected hybrid energy storage system, which is one of the main contributions to the current NSFC projects as well as a research proposal of a new awarded NSFC project starting from 2020.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -