Data-driven optimisation to enhance train/tram power systems

This research project aims to enhance the reliability, efficiency, and sustainability of railway traction power systems using data-driven optimisation methods, such as machine learning.

An in-depth analysis of the Melbourne tram and train power systems will be conducted to assess their performance and identify knowledge gaps, including peak load behaviour and energy consumption patterns.

By addressing these gaps, the project aims to establish practical methodologies for optimising and enhancing railway traction power systems.

Additionally, the project will evaluate the impact of the train traction power system on the electrical grid network.

Through comprehensive analyses, machine learning model development, and proposed infrastructure improvements, the project aims to provide actionable insights for a sustainable future in rail transport.

Participants

• Centre for New Energy Technologies (C4NET)
• Department of Transport and Planning (Victoria)
• RMIT University

Project background

The increasing patronage demand and growing environmental concerns have highlighted the significance of optimising the performance, efficiency, and reliability of railway traction power systems.

The sustainability and efficient operation of trams and trains depend heavily on these systems.

However, the complex interactions between load performance, tram and train operations, and fault conditions need thorough study and understanding.

This project aims to develop a comprehensive understanding of the performance of Melbourne’s tram and train traction power system using currently available data and information. It will also investigate potential solutions for improving efficiency and reliability.

The complexity and numerous influential parameters in tram and train traction power systems make it challenging to accurately understand, model and simulate their performance.

By employing advanced data-driven-based modelling algorithms, Monte Carlo simulations, and Pareto optimisation techniques, the project will iteratively analyse interactions and dependencies, refining parameter values until a comprehensive and optimal solution is attained.

This process will provide deeper insights into the performance and reliability of tram and train traction power systems, helping to identify areas for improvement and enabling more informed decisions about system optimisation.

Data-driven methods are more efficient and effective in addressing the complexities of tram and traction power systems, enhancing their overall performance, reliability, and energy efficiency.

Project objectives

The primary objectives of this research are as follows:

• Gain an in-depth knowledge of Melbourne’s tram and train traction power systems.
• Develop and apply a data-driven optimisation method to enhance the performance and energy efficiency of Melbourne’s train traction power system.
• Achieve a comprehensive understanding of the benefits of the optimised traction power system on the electrical grid by increasing the reliability and reducing the energy consumption of tram/train network.
• Establish baseline modelling to inform future co-optimisation of transport systems with the operation and design of electricity distribution systems.
• Use data-driven insights to provide robust recommendations for optimised and future-proofed train infrastructure and energy systems.
• Provide recommendations on best practices for enhancing or optimising the tram network’s traction power system.

Please note …

This page will be a living record of this project. As it matures, hits milestones, etc., we’ll continue to add information, links, images, interviews and more. Watch this space!