Cellulose Nanofibrils (CNFs), highly present in nature, can be used as building blocks for future sustainable materials, including strong and stiff filaments. A rheo-optical flow-stop technique is used to conduct experiments to characterize the CNFs by studying Brownian dynamics through the CNFs' birefringence decay after stop. As the experiments produce large quantities of data, we reduce their dimensionality using Principal Component Analysis (PCA) and exploit the possibility of visualizing the reduced data in two ways. First, we plot the principal components (PCs) as time series, and by training LSTM networks assigned for each PC time series with the data before the flow stop, we predict the behavior after the flow stop (Bragone et al., 2024). Second, we plot the first PCs against each other to create clusters that give information about the different CNF materials and concentrations. Our approach aims at classifying the CNF materials to varying concentrations by applying unsupervised machine learning algorithms, such as 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.

Transformers are crucial for reliable and efficient power system operations, particularly in supporting the integration of renewable energy. Effective monitoring of transformer health is critical to maintain grid stability and performance. Thermal insulation ageing is a key transformer failure mode, which is generally tracked by monitoring the hotspot temperature (HST). However, HST measurement is complex, costly, and often estimated from indirect measurements. Existing HST models focus on space-agnostic thermal models, providing worst-case HST estimates. This article introduces a spatio-temporal model for transformer winding temperature and ageing estimation, which leverages physics-based partial differential equations (PDEs) with data-driven Neural Networks (NN) in a Physics Informed Neural Networks (PINNs) configuration to improve prediction accuracy and acquire spatio-temporal resolution. The computational accuracy of the PINN model is improved through the implementation of the Residual-Based Attention (PINN-RBA) scheme that accelerates the PINN model convergence. The PINN-RBA model is benchmarked against self-adaptive attention schemes and classical vanilla PINN configurations. For the first time, PINN based oil temperature predictions are used to estimate spatio-temporal transformer winding temperature values, validated through PDE numerical solution and fiber optic sensor measurements. Furthermore, the spatio-temporal transformer ageing model is inferred, which supports transformer health management decision-making. Results are validated with a distribution transformer operating on a floating photovoltaic power plant.

Aviation and maritime shipping are hard-to-abate transport sectors that are heavily dependent on fossil fuels. They jointly account for nearly 10 % of global greenhouse gas emissions, while infrastructure and investments are locked into high-carbon pathways for decades. Fuels and technologies to decarbonize include advanced biofuels, electrofuels, hydrogen and electric propulsion. This research aims to analyse the decarbonization strategies for maritime shipping and aviation from a comparative perspective, and analyzing the role of different actors for disruption to break through carbon lock-in and path dependency. The research uses Sweden as a case study and applies qualitative methods, including expert interviews, focus group discussions and site visits. Our research finds that aviation and maritime shipping are slowly changing, albeit with different dynamics. Both sectors show that incumbent regime actors play a major role in shaping transition pathways and disrupting the (quasi)equilibrium, while niche innovation is often developed together by incumbents and niche players.

Dynamic rating of the transformer is a promising technology, which is suitable for various applications. Using dynamic rating for connecting renewable energy is believed to be beneficial for the economy and flexibility of the power system. However, to safely deploy such operation strategies, it is important to have more precise estimates for the total costs of owning such units and determine how effective such operation method is for a solar power plant. This study proposes a method for calculating total ownership costs (TOC) of dynamically rated transformers used for the connection of the solar power plant to the grid as well as analyzes its efficiency. The sensitivity analysis looks into the change in TOC and peak efficiency index (PEI) after considering reactive power dispatch. Results of this study also show how TOC, PEI, and load and no-load losses change depending on the transformer size.

Dynamic Transformer Rating (DTR) allows unlocking extra capacity of power transformers using real-time weather data and has been proven to be specifically attractive for application to wind farm substation transformers. In this work, we explore an extreme case where the wind farm expanded to 150% of its original rated power while being connected to the grid with the same transformer to simulate a 1:1.5 ratio between rated generation and rated transformer capacity. The focus of the study is to explore the operational challenges of using such a system in a framework of day-ahead dispatch planning, which is done by building a combined forecasting model for 36-hour ahead prediction of wind farm generation and the transformer capacity as well as their match together. The goal is to estimate how often the wind farm generation would exceed the available capacity at the substation and would be required to curtail as well as assign accuracy to the curtailment decision. The results indicate that the model shows sufficient prediction accuracy for exceeding the maximum allowable transformer temperature. Main indication of the model accuracy is the ability to correctly predict instances of transformer overheating, which in this case are below 3.5%. However, since the accuracy of correctly ordered curtailment is at 85% for the lower transformer hot spot temperature limit, future studies should focus on improving current results by possibly integrating other time series forecasting models.

As the quest for more sustainable and environmentally friendly materials has increased in the last decades, cellulose nanofibrils (CNFs), abundant in nature, have proven their capabilities as building blocks to create strong and stiff filaments. Experiments have been conducted to characterize CNFs with a rheo-optical flow-stop technique to study the Brownian dynamics through the CNFs’ birefringence decay after stop. This paper aims to predict the initial relaxation of birefringence using Principal Component Analysis (PCA) and Long Short-Term Memory (LSTM) networks. By reducing the dimensionality of the data frame features, we can plot the principal components (PCs) that retain most of the information and treat them as time series. We employ LSTM by training with the data before the flow stops and predicting the behavior afterward. Consequently, we reconstruct the data frames from the obtained predictions and compare them to the original data.

An increase in electricity demand and renewable penetration requires electrical utilities to improve and optimize the grid infrastructure. Fundamental components in this grid infrastructure are transformers, which are designed conservatively based on static rated power. However, load and weather change continuously and hence, transformers are not used most efficiently. For this reason, new technology has been developed: Dynamic transformer rating (DTR). Applying DTR makes it possible to load transformers above the nameplate rating without affecting their lifetime expectancy. This study uses DTR for short-term and long-term wind farm planning. The optimal wind farm is designed by applying DTR to the power transformer and using it as an input to a Mixed-Integer Linear Programming (MILP) model. Regarding the transformer thermal analysis, the linearized top oil model of IEEE Clause 7 is selected. The model is executed for 4 different types of power transformers: 63 MVA, 100 MVA, 200 MVA and 400 MVA. As a result, it is obtained that the net present value for the investment and the capacity of the wind farm increase linearly with respect to the size of the transformer. Then, a sensitivity analysis is carried out by modifying the wind speed, the electricity price, the lifetime of the transformer and the selected weather data. From this sensitivity analysis, it is possible to conclude that wind resources and electricity price are critical parameters for the wind farm’s feasibility.

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

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.

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.