Thermal Constrained Energy Optimization of Railway Cophase Systems With ESS Integration—An FRA-Pruned DQN Approach
Bibliographic record
Abstract
This article investigates the railway cophase 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 cophase 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-learning network (DQN)-based thermal constrained energy management strategy in this article. First, 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".