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Enhancing energy management of a stationary energy storage system in a DC electric railway using fuzzy logic control.

Authors :
Alnuman, Hammad H.
Gladwin, Daniel T.
Foster, Martin P.
Ahmed, Emad M.
Source :
International Journal of Electrical Power & Energy Systems. Nov2022:Part B, Vol. 142, pN.PAG-N.PAG. 1p.
Publication Year :
2022

Abstract

In DC electric railways, energy storage systems (ESSs) have been addressed to assist in the energy efficiency improvement, which is achieved by exploiting the captured excess braking energy of decelerating trains in order to reduce the traction energy demand of the accelerating trains. Conventionally, stationary ESSs are assumed to have access to the substations and grid, which is being a significant shortcoming since the ESS in many locations such as the London Underground has neither access to the traction substations nor external sources. Since ESSs cannot have infinite capacity and have practical restrictions on the state of charge (SOC), much of the regenerative energies during braking are being lost, which reduces the benefits of ESSs in enhancing energy efficiency of electric railways. In this paper, boosting the energy efficiency of the system is achieved by proposing a new adaptive control method based on fuzzy logic control (FLC) to dynamically control a stationary ESS connected to a DC track system. Therefore, the power flow between the track and the ESS with respect to the storage capacity is managed without affecting the benefits yielded by the system. The proposed control method is simulated and tested experimentally on a real prototype including a supercapacitor ESS. Three traffic scenarios are applied to evaluate the feasibility and the performance of the proposed controller with respect to the undefined traffic scenarios matching the case in the London Underground. The proposed control method applied to three traffic scenarios have achieved energy savings of 39.2–41.4%. The experimental results are compared against the simulation results, showing a percentage error ranging from 1.8 to 3.6%. © 2017 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01420615
Volume :
142
Database :
Academic Search Index
Journal :
International Journal of Electrical Power & Energy Systems
Publication Type :
Academic Journal
Accession number :
157501774
Full Text :
https://doi.org/10.1016/j.ijepes.2022.108345