Xing, C, Li, K orcid.org/0000-0001-6657-0522 and Su, J (2023) Thermal Constrained Energy Optimization of Railway Co-phase Systems with ESS Integration - An FRA-pruned DQN Approach. IEEE Transactions on Transportation Electrification, 9 (4). 5122 -5139. ISSN 2332-7782
Abstract
This paper investigates the railway co-phase traction power supply system (TPSS) with a power flow controller (PFC) to address the power quality and neutral section issues. To collect the regenerative energy and achieve a more flexible power flow, the energy storage system (ESS) is integrated into the co-phase system. As the key components, the reliability of power electronics modules in PFC and battery cells in ESS is highly related to their thermal performance. It is therefore vital to consider their operational thermal dynamics, leading to the proposal of a deep Q network (DQN) based thermal constrained energy management strategy in this paper. Firstly, the system power flow model and electrothermal models for power electronics modules and battery cells are all established. Then, a DQN method is adopted to learn an optimized policy for peak power shaving while meetings thermal constraints. Finally, an FRA-based pruning method is proposed to reshape the agent to become more compact without sacrificing its performance. Case studies confirm that the proposed strategy can effectively reduce the peak traction power supply by up to 42.0%, and achieve up to 94.1% thermal reduction. The FRA-based pruning can achieve up to 89.9% agent size reduction and 87.2% computation savings.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Co-phase railway traction system; thermal constraint; deep Q-learning network; agent pruning |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Funding Information: | Funder Grant number Ofgem Not Known SP Transmission PLC Not Known |
Depositing User: | Symplectic Publications |
Date Deposited: | 31 Oct 2022 10:59 |
Last Modified: | 22 Aug 2024 13:40 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Identification Number: | 10.1109/TTE.2022.3218762 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:192745 |