The University of Southampton
University of Southampton Institutional Repository

Intelligently controlled flywheel storage for enhanced dynamic performance

Intelligently controlled flywheel storage for enhanced dynamic performance
Intelligently controlled flywheel storage for enhanced dynamic performance
This paper investigates the development and application of a nonlinear adaptive intelligent controller with superior disturbance-rejection capability for a doubly-fed-inductionmachine driven flywheel energy storage system (FESS), to mitigate the intermittency in wind power injection, as well as enhance the transient stability of the connected multimachine power system thereby isolating the grid power from fluctuations. Intelligent supervisors for the rotor-side-converter of the FESS have been constructed using a fuzzy rule set. The consequent part in every rule includes a wavelet function for function learning. The novel normalized gradient descent algorithm with adaptive learning rate has been used to derive the update laws for unknown controller parameters. The control law has been derived by minimizing the predictive performance index. Convergence of the developed algorithm is guaranteed as proven via the Lyapunov stability method. Realistic wind speed profile has been used to test the efficacy of the developed control scheme. The sizing strategy has been devised for optimal sizing of FESS for its efficient utilization. Modified WSCC nine-bus test system has been used for the nonlinear timedomain simulations and novel indices have been used for evaluation of controller performance against its predecessors. OP 5600 RealTime-Digital-Simulator has been used to demonstrate the real-time implementation of the suggested scheme.
1949-3029
2163 - 2173
Mir, Abdul Saleem
491bb457-cbe5-4705-ab36-860b78763332
Senroy, Nilanjan
b4565b4f-78eb-4a88-8af9-9d8c29238a4d
Mir, Abdul Saleem
491bb457-cbe5-4705-ab36-860b78763332
Senroy, Nilanjan
b4565b4f-78eb-4a88-8af9-9d8c29238a4d

Mir, Abdul Saleem and Senroy, Nilanjan (2018) Intelligently controlled flywheel storage for enhanced dynamic performance. IEEE Transactions on Sustainable Energy, 10 (4), 2163 - 2173. (doi:10.1109/TSTE.2018.2881317).

Record type: Article

Abstract

This paper investigates the development and application of a nonlinear adaptive intelligent controller with superior disturbance-rejection capability for a doubly-fed-inductionmachine driven flywheel energy storage system (FESS), to mitigate the intermittency in wind power injection, as well as enhance the transient stability of the connected multimachine power system thereby isolating the grid power from fluctuations. Intelligent supervisors for the rotor-side-converter of the FESS have been constructed using a fuzzy rule set. The consequent part in every rule includes a wavelet function for function learning. The novel normalized gradient descent algorithm with adaptive learning rate has been used to derive the update laws for unknown controller parameters. The control law has been derived by minimizing the predictive performance index. Convergence of the developed algorithm is guaranteed as proven via the Lyapunov stability method. Realistic wind speed profile has been used to test the efficacy of the developed control scheme. The sizing strategy has been devised for optimal sizing of FESS for its efficient utilization. Modified WSCC nine-bus test system has been used for the nonlinear timedomain simulations and novel indices have been used for evaluation of controller performance against its predecessors. OP 5600 RealTime-Digital-Simulator has been used to demonstrate the real-time implementation of the suggested scheme.

Full text not available from this repository.

More information

Published date: 14 November 2018

Identifiers

Local EPrints ID: 449247
URI: http://eprints.soton.ac.uk/id/eprint/449247
ISSN: 1949-3029
PURE UUID: 4fa55f78-8e9b-4f5c-b2dd-f6e76480f244

Catalogue record

Date deposited: 20 May 2021 16:32
Last modified: 20 May 2021 16:32

Export record

Altmetrics

Contributors

Author: Abdul Saleem Mir
Author: Nilanjan Senroy

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×