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Using Physics-Informed Machine Learning for reusing power system components

Reference number
Coordinator Kungliga Tekniska Högskolan - KTH Skolan för elektroteknik och datavetenskap
Funding from Vinnova SEK 4 000 000
Project duration November 2021 - November 2025
Status Ongoing
Venture Circular and biobased economy
Call Increased resource efficiency for a circular industry

Purpose and goal

The purpose of the project is to use innovative computational methods and take advantage of physics-informed neural networks (PINNs) to study the ageing of power components in a wind farm and find possible applications for their reuse. The goals of the project are to: - use PINNs to predict the component ageing - study potential good applications for the method - identify methods for monitoring older components to ensure the system´s safety

Expected results and effects

Expected results: - well-trained PINNs, which help to define and predict ageing of power components in a wind farm - proposed strategies for reuse and upcycling of power components in new applications - discuss possible reliability impacts of the new application Expected effects: - novel end of life management strategies can reduce negative climate impacts of the renewable energy development - Swedish and international businesses will be able to use our methodology to reduce capital investment on wind farms as well as get extra return on investment after wind farm decommissioning

Planned approach and implementation

This project has started on the 1st of November 2021 and will continue until the 30th of June 2026. The project will consist of 7 working packages. 1st working package will focus on identifying key assets for the study. WP 2 and 3 will be oriented towards the development of PINN methods and will compose the longest project stage. WP 4,5 and 6 will test the methods on several case studies and suggest possible new applications for the assets as well as give an economic evaluation of the results. WP 7 will focus on communication and dissemination of the project results.

The project description has been provided by the project members themselves and the text has not been looked at by our editors.

Last updated 15 November 2021

Reference number 2021-03748

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