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  • 1.
    Bragone, Federica
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Physics-Informed Neural Networks and Machine Learning Algorithms for Sustainability Advancements in Power Systems Components2023Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    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. 

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    Summary
  • 2.
    Laneryd, Tor
    et al.
    Hitachi Energy, Hitachi Energy Res, Västerås, Sweden..
    Bragone, Federica
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Morozovska, Kateryna
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Luvisotto, Michele
    Hitachi Energy, Hitachi Energy Res, Västerås, Sweden..
    Physics Informed Neural Networks for Power Transformer Dynamic Thermal Modelling2022In: IFAC Papersonline, Elsevier BV , 2022, Vol. 55, no 20, p. 49-54Conference paper (Refereed)
    Abstract [en]

    The emerging methodology of Physics Informed Neural Networks (PINNs) promises to combine available data and physical knowledge to achieve high accuracy and fast evaluation. Dynamic thermal modelling of power transformers is an application specifically set to benefit from these characteristics. Data collected during typical operation is not representative of extreme loading scenarios and the number of thermal sensors is limited. The detailed geometry is often not known by the asset owner which creates high uncertainty for physics-based simulation models. In this study, the transformer is modeled by the one-dimensional heat diffusion equation. PINN is constructed with a loss function including both data-based and physics-based terms. A time-dependent source term from a time series of measurement is also part of the PINN. The result is compared with a finite volume solution demonstrating good agreement. The PINN approach will be useful for further development in thermal modelling for power transformers. Copyright

  • 3.
    Bragone, Federica
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Oueslati, Khaoula
    Hitachi Energy, Västerås, Sweden.
    Laneryd, Tor
    Hitachi Energy, Västerås, Sweden.
    Luvisotto, Michele
    Hitachi Energy, Västerås, Sweden.
    Morozovska, Kateryna
    KTH, School of Industrial Engineering and Management (ITM), Industrial Economics and Management (Dept.), Sustainability, Industrial Dynamics & Entrepreneurship.
    Physics-Informed Neural Networks for Modeling Cellulose Degradation in Power Transformers2022In: 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA), IEEE conference proceedings, 2022Conference paper (Refereed)
    Abstract [en]

    Insulation is an essential part of power transformers, which guarantees an efficient and reliable operational life. It mainly consists of mineral oil and insulation paper. Most of the major failures of power transformers originate from internal insulation failures. Monitoring aging and thermal behaviour of the transformer’s insulation paper is achieved by different techniques, which consider the Degree of Polymerization (DP) to evaluate the cellulose degradation and other chemical factors accumulated in mineral oil. Given the physical and chemical nature of the problem of degradation, we couple it with machine learning models to predict the desired parameters for considered equations. In particular, the equation used applies the Arrhenius relation, which comprises parameters like the pre-exponential factor, which depends on the cellulose’s contamination content, and the activation energy, which is connected to the temperature dependence; both of the factors need to be estimated for our problem. For this reason, Physics-Informed Neural Networks (PINNs) are considered for solving the data-driven discovery of the DP equation.

  • 4.
    Oueslati, Khaoula
    et al.
    National Institute of Applied Sciences and Technology, MMA Lab University of Carthage, Tunis, Tunisia.
    Dhahbi-Megriche, Nabila
    National Institute of Applied Sciences and Technology, MMA Lab University of Carthage, Tunis, Tunisia.
    Bragone, Federica
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Morozovska, Kateryna
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electromagnetic Engineering. KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Lanerys, Tor
    Hitachi Energy, Hitachi Energy Res, Västerås, Sweden.
    Luvisotto, Michele
    Hitachi Energy, Hitachi Energy Res, Västerås, Sweden.
    Physics-Informed Neural Networks for modelling insulation paper degradation in Power Transformers2022In: 2022 IEEE International Conference on Electrical Sciences and Technologies in Maghreb (CISTEM), 2022Conference paper (Refereed)
    Abstract [en]

    Power transformer’s insulation is an integral part of the health and performance of this power component. This paper uses Physics-Informed Neural Networks (PINNs) for predicting the lifetime and health indicator of the power transformer’s insulation material, which is expressed as the Degree of Polymerization (DP) of the polymeric material (in this case kraft paper). PINNs are a promising deep learning technique for solving scientific computing problems and are designed to incorporate prior knowledge of physical or chemical systems and to respect any symmetries, invariances, and conservation laws. The dynamics of the degradation process is modeled using ordinary differential equations. One major challenge in analyzing kraft paper degradation is estimating the unknown model parameters (e.g. rate constants) and thus predicting model dynamics. For this work, we aim to solve the data-driven discovery of the degradation process, infer the hidden kinetic parameters and predict the degree of polymerization. The final discussion also addresses the advantages and limitations of PINNs for solving this type of problems.

  • 5.
    Bragone, Federica
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Morozovska, Kateryna
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Hilber, Patrik
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electromagnetic Engineering.
    Laneryd, Tor
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering.
    Luvisotto, Michele
    Hitachi Energy.
    Physics-informed neural networks for modelling power transformer’s dynamic thermal behaviour2022In: Electric power systems research, ISSN 0378-7796, E-ISSN 1873-2046, Vol. 211, p. 108447-108447, article id 108447Article in journal (Refereed)
    Abstract [en]

    This paper focuses on the thermal modelling of power transformers using physics-informed neural networks (PINNs). PINNs are neural networks trained to consider the physical laws provided by the general nonlinear partial differential equations (PDEs). The PDE considered for the study of power transformer’s thermal behaviour is the heat diffusion equation provided with boundary conditions given by the ambient temperature at the bottom and the top-oil temperature at the top. The model is one dimensional along the transformer height. The top-oil temperature and the transformer’s temperature distribution are estimated using field measurements of ambient temperature, top-oil temperature and the load factor. The measurements from a real transformer provide more realistic solution, but also an additional challenge. The Finite Volume Method (FVM) is used to calculate the solution of the equation and further to benchmark the predictions obtained by PINNs. The results obtained by PINNs for estimating the top-oil temperature and the transformer’s thermal distribution show high accuracy and almost exactly mimic FVM solution.

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  • 6. Welin Odeback, Oliver
    et al.
    Bragone, Federica
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Laneryd, Tor
    Hitachi Energy, Hitachi Energy Res, Västerås, Sweden.
    Luvisotto, Michele
    Hitachi Energy, Hitachi Energy Res, Västerås, Sweden.
    Morozovska, Kateryna
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electromagnetic Engineering. KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Physics-Informed Neural Networks for prediction of transformer’s temperature distribution2022In: 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA), IEEE, 2022Conference paper (Refereed)
    Abstract [en]

    Physics-Informed Neural Networks (PINNs) are a novel approach to the integration of physical models into Neural Networks when solving supervised learning problems. PINNs have shown potential in mapping spatio-temporal input and the solution of a partial differential equation (PDE). However, despite their advantages for many applications, they often fail to train when target PDEs contain high frequencies or multi-scale features. Thermal modelling of power transformers is fundamental for improving their efficiency and extending their lifetime. In this work, we investigate the performance of different PINN architectures applied to a 1D heat diffusion equation with a specific heat source representing the heat distribution inside a transformer. Measurements, which include the top-oil temperature, the ambient temperature and the load factor are taken from a transformer in service. We demonstrate the limitations of PINNs, propose possible remedies, and provide an overall assessment of the potential of using PINNs for transformer thermal modelling.

  • 7.
    Welin Odeback, Oliver
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science.
    Bragone, Federica
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science.
    Laneryd, Tor
    Hitachi Energy, Västerås, Sweden..
    Luvisotto, Michele
    Hitachi Energy, Västerås, Sweden..
    Morozovska, Kateryna
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science.
    Physics-Informed Neural Networks for prediction of transformer's temperature distribution2022In: 2022 21ST IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, ICMLA / [ed] Wani, MA Kantardzic, M Palade, V Neagu, D Yang, L Chan, KY, Institute of Electrical and Electronics Engineers (IEEE) , 2022, p. 1579-1586Conference paper (Refereed)
    Abstract [en]

    Physics-Informed Neural Networks (PINNs) are a novel approach to the integration of physical models into Neural Networks when solving supervised learning problems. PINNs have shown potential in mapping spatio-temporal input and the solution of a partial differential equation (PDE). However, despite their advantages for many applications, they often fail to train when target PDEs contain high frequencies or multiscale features. Thermal modelling of power transformers is fundamental for improving their efficiency and extending their lifetime. In this work, we investigate the performance of different PINN architectures applied to a 1D heat diffusion equation with a specific heat source representing the heat distribution inside a transformer. Measurements, which include the top-oil temperature, the ambient temperature and the load factor are taken from a transformer in service. We demonstrate the limitations of PINNs, propose possible remedies, and provide an overall assessment of the potential of using PINNs for transformer thermal modelling.

  • 8.
    Bogatov Wilkman, Dennis
    et al.
    KTH. Atlas Copco ITBA .
    Tang, Lifei
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Integrated Product Development. Atlas Copco ITBA.
    Morozovska, Kateryna
    KTH, School of Industrial Engineering and Management (ITM), Industrial Economics and Management (Dept.), Sustainability, Industrial Dynamics & Entrepreneurship.
    Bragone, Federica
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Self-Supervised Transformer Networks for Error Classification of Tightening Traces2022In: 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA), IEEE conference proceedings, 2022Conference paper (Refereed)
    Abstract [en]

    Transformers have shown remarkable results in the domains of Natural Language Processing and Computer Vision. This naturally raises the question of whether the success could be replicated in other domains. However, due to Transformers being inherently data-hungry and sensitive to weight initialization, applying the Transformer to new domains is quite a challenging task. Previously, the data demands have been met using large-scale supervised or self-supervised pre-training on a similar task before supervised fine-tuning on a target downstream task. We show that Transformers are applicable for the task of multi-label error classification of trace data and that masked data modelling based on self-supervised learning methods can be used to leverage unlabelled data to increase performance compared to a baseline supervised learning approach.

  • 9.
    Bragone, Federica
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Rosén, Tomas
    KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Fibre- and Polymer Technology. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Centres, Wallenberg Wood Science Center.
    Morozovska, Kateryna
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST). KTH, School of Industrial Engineering and Management (ITM), Industrial Economics and Management (Dept.), Sustainability, Industrial Dynamics & Entrepreneurship.
    Laneryd, Tor
    Hitachi Energy, Västerås, Sweden.
    Söderberg, Daniel
    KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Centres, Wallenberg Wood Science Center. KTH, School of Engineering Sciences (SCI), Engineering Mechanics. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Fibre- and Polymer Technology, Fiberprocesser.
    Markidis, Stefano
    KTH, Centres, SeRC - Swedish e-Science Research Centre. KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Unsupervised Learning Analysis of Flow-Induced Birefringence in Nanocellulose: Differentiating Materials and ConcentrationsManuscript (preprint) (Other academic)
    Abstract [en]

    Cellulose nanofibrils (CNFs) can be used as building blocks for future sustainable materials including strong and stiff filaments. The goal of this paper is to introduce a data analysis of flow-induced birefringence experiments by means of unsupervised learning techniques. By reducing the dimensionality of the data with Principal Component Analysis (PCA) we are able to exploit information for the different cellulose materials at several concentrations and compare them to each other. Our approach aims at classifying the CNF materials at different concentrations by applying unsupervised machine learning algorithms, like k-means and Gaussian Mixture Models (GMMs). Finally, we analyze the autocorrelation function (ACF) and the partial autocorrelation function (PACF) of the first principal component, detecting seasonality in lower concentrations. The focus is given to the initial relaxation of birefringence after the flow is stopped to have a better understanding of the Brownian dynamics for the given materials and concentrations.

    Our method can be used to distinguish the different materials at specific concentrations and could help to identify possible advantages and drawbacks of one material over the other. 

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