| 1. |
- Ariza Rocha, Oscar David, et al.
(author)
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Dynamic rating assists cost-effective expansion of wind farms by utilizing the hidden capacity of transformers
- 2020
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In: International Journal of Electrical Power & Energy Systems. - : Elsevier. - 0142-0615 .- 1879-3517. ; 123
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Journal article (peer-reviewed)abstract
- Dynamic rating of power transmission devices is a technology that allows better equipment utilization through real-time monitoring of the weather conditions and the load. Dynamic rating of transformers is a fairly new technology if compared to the dynamic rating of power lines, and has a high potential for significantly improving component utilization while lowering investment costs on installing new transformers.The following work investigates how to utilize already operational transformers, which are used for wind farm connection, for expanding wind generation capacity. Also, this paper shows improvements that dynamic transformer rating can bring to both power grid operators and wind farm owners by exploring the economic benefits of expanding wind parks without investing in new power transformers. Connecting additional wind turbines at sites with high wind potential after the wind park is already in exploitation can assist in lowering electricity price and provide a possibility of less risky investment in the wind energy sector. This paper uses transformer thermal modelling and wind farm expansion techniques such as convolution method and product method to investigate to which extent existing wind farms can be expanded using already installed transformer units.Five transformer locations and nine units are studied for finding the potential of dynamic transformer rating for network expansion applications. The analysis shows that the optimal expansion of wind power from a generator perspective is around 30%" role="presentation" style="box-sizing: border-box; margin: 0px; padding: 0px; display: inline-block; line-height: normal; font-size: 16.2px; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; position: relative;">% to 50%" role="presentation" style="box-sizing: border-box; margin: 0px; padding: 0px; display: inline-block; line-height: normal; font-size: 16.2px; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; position: relative;">%, although, it can be limited further by network restrictions. A possibility to use a large component, such as power transformer, closer to its full potential can provide material and cost savings for building new devices and decrease investment costs on manufacturing, transportation and installation of new units. Dynamic rating of power transformers can also increase the socio-economic benefits of renewable energy by lowering electricity price from renewables and incentivize an increased share of green power in electricity markets.
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| 2. |
- Ariza Rocha, Oscar David, et al.
(author)
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Dynamic rating assists cost-effective expansion of wind farms byutilizing hidden capacity of transformers
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Other publication (other academic/artistic)abstract
- Dynamic rating of power transmission devices is a technology that allows better equipment utilization through real-time information about the system state. Dynamic rating of transformers is a fairly new technology if compared to dynamic rating of power lines, and has high potential for significantly improving component utilization while lowering investment costs on installing new transformers.Dynamic transformer rating increases the rating of the transformer considering load and temperature variations without affecting safe operation. Dynamic rating is highly suitable for being used in conjunction with renewable energy generation, specifically wind power. The following work investigates how to utilize existing transformers, which are under exploitation at wind farms, for expanding wind generation capacity. Also, this paper shows improvements that dynamic rating can bring to both power grid operators and wind farm owners by exploring the economic benefits of expanding wind parks while using dynamic rating. Connecting additional wind turbines with the same transformer at sites with high wind capacity after the wind park is already in exploitation can assist in lowering electricity price and provide a possibility of less risky investment in wind power.Five transformer locations and nine units are studied for finding the potential of dynamic transformer rating for network expansion applications. The analysis shows that the optimal expansion of wind power from a generator perspective is around 30 % to 50 %, although, it can be limited further by network restrictions. A possibility to use a large device, suchas power transformer, closer to its full potential can provide material and cost savings for building new devices and decrease investment costs on manufacturing, transportation and installation of new units. Dynamic rating of power transformers can also increase the socio-economic benefits of renewable energy by lowering electricity price from renewables and incentivize an increased share of green power in electricity markets.
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| 3. |
- Bogatov Wilkman, Dennis, et al.
(author)
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Self-Supervised Transformer Networks for Error Classification of Tightening Traces
- 2022
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In: 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA). - : IEEE conference proceedings.
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Conference paper (peer-reviewed)abstract
- 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.
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| 4. |
- Bragone, Federica
(author)
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Physics-Informed Neural Networks and Machine Learning Algorithms for Sustainability Advancements in Power Systems Components
- 2023
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Licentiate thesis (other academic/artistic)abstract
- 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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| 5. |
- Bragone, Federica, et al.
(author)
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Physics-Informed Neural Networks for Modeling Cellulose Degradation in Power Transformers
- 2022
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In: 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA). - : IEEE conference proceedings.
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Conference paper (peer-reviewed)abstract
- 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.
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| 6. |
- Bragone, Federica, et al.
(author)
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Physics-informed neural networks for modelling power transformer’s dynamic thermal behaviour
- 2022
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In: Electric power systems research. - : Elsevier. - 0378-7796 .- 1873-2046. ; 211, s. 108447-108447
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Journal article (peer-reviewed)abstract
- 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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| 7. |
- Bragone, Federica, et al.
(author)
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Time Series Predictions Based on PCA and LSTM Networks: A Framework for Predicting Brownian Rotary Diffusion of Cellulose Nanofibrils
- 2024
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In: Computational Science – ICCS 2024 - 24th International Conference, 2024, Proceedings. - : Springer Nature. ; , s. 209-223
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Conference paper (peer-reviewed)abstract
- 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.
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| 8. |
- Bragone, Federica, et al.
(author)
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Unsupervised Learning Analysis of Flow-Induced Birefringence in Nanocellulose: Differentiating Materials and Concentrations
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Other publication (other academic/artistic)abstract
- 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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| 9. |
- Calil, Wílerson Venceslau, et al.
(author)
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Determining total cost of ownership and peak efficiency index of dynamically rated transformer at the PV-power plant
- 2024
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In: Electric power systems research. - : Elsevier BV. - 0378-7796 .- 1873-2046. ; 229
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Journal article (peer-reviewed)abstract
- 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.
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| 10. |
- Chakrapani Manakari, Vageesh, et al.
(author)
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Minimization of Wind Power Curtailment using Dynamic Line Rating
- 2020
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In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe).
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Conference paper (peer-reviewed)abstract
- Large scale penetration of wind power to the grid and unevenly spread power demand can result in the transmission system not being able to dispatch all the produced wind power, causing in wind power curtailment.Dynamic line rating (DLR) is a technology which uses thermal properties of overhead conductors and weather data to determine the real-time ampacity limits of transmission lines. In general, dynamic rating allows extending capacity limits of power lines and helps to remove congestion in the grid.(/p)(p This study looks into the possibility of using dynamic line rating for removing congestion in the power system to allow dispatching more wind power and minimize the need for curtailment. The results of case-studies have shown that DLR allows to significantly reduce the curtailment of generation, especially during winter, when wind production is highest and day time, when the power demand is the highest.
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