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Physics-Informed Neural Networks and Machine Learning Algorithms for Sustainability Advancements in Power Systems Components

Time: Fri 2023-11-03 09.00

Location: Room 3412, Sten Velander, Teknikringen 33

Video link:

Language: English

Subject area: Computer Science

Doctoral student: Federica Bragone , Beräkningsvetenskap och beräkningsteknik (CST)

Opponent: Matthias Ehrhardt, University of Wuppertal, Wuppertal, Germany

Supervisor: Stefano Markidis, SeRC - Swedish e-Science Research Centre, Beräkningsvetenskap och beräkningsteknik (CST); Kateryna Morozovska, Beräkningsvetenskap och beräkningsteknik (CST), Hållbarhet, Industriell dynamik & entreprenörskap; Tor Laneryd, Hitachi Energy, Västerås, Sweden; Michele Luvisotto, Hitachi Energy, Västerås, Sweden

QC 20231010


A power system consists of several critical components necessary for providing electricity from the producers to the consumers. Monitoring the lifetime of power system components becomes vital since they are subjected to electrical currents and high temperatures, which affect their ageing. Estimating the component's ageing rate close to the end of its lifetime is the motivation behind our project. Knowing the ageing rate and life expectancy, we can possibly better utilize and re-utilize existing power components and their parts. In return, we could achieve better material utilization, reduce costs, and improve sustainability designs, contributing to the circular industry development of power system components. Monitoring the thermal distribution and the degradation of the insulation materials informs the estimation of the components' health state. Moreover, further study of the employed paper material of their insulation system can lead to a deeper understanding of its thermal characterization and a possible consequent improvement.

Our study aims to create a model that couples the physical equations that govern the deterioration of the insulation systems of power components with modern machine learning algorithms. 

As the data is limited and complex in the field of components' ageing, Physics-Informed Neural Networks (PINNs) can help to overcome the problem. PINNs exploit the prior knowledge stored in partial differential equations (PDEs) or ordinary differential equations (ODEs) modelling the involved systems. This prior knowledge becomes a regularization agent, constraining the space of available solutions and consequently reducing the training data needed. 

This thesis is divided into two parts: the first focuses on the insulation system of power transformers, and the second is an exploration of the paper material concentrating on cellulose nanofibrils (CNFs) classification. The first part includes modelling the thermal distribution and the degradation of the cellulose inside the power transformer. The deterioration of one of the two systems can lead to severe consequences for the other. Both abilities of PINNs to approximate the solution of the equations and to find the parameters that best describe the data are explored. The second part could be conceived as a standalone; however, it leads to a further understanding of the paper material. Several CNFs materials and concentrations are presented, and this thesis proposes a basic unsupervised learning using clustering algorithms like k-means and Gaussian Mixture Models (GMMs) for their classification.