New Innovations in eIDAS-compliant Trust Services: Blockchain

Los avances tecnológicos van a pasos agigantados, con ellos marcan nuevas tendencias que emergen para dominar el mercado, productos que antes era novedosos y que ahora deben adaptarse para seguir siendo competitivos. Por ello, el equipo compuesto por 3 estudiantes de la FIB ¿ UPC (Arthur Bernal, Marc Méndez y Xiaolei Lin) y dirigido por el profesor y director Francisco Jordan proponen en este proyecto nuevas tecnologías innovadoras que marcará el futuro tecnológico e incorporarlo en el producto TrustedX. Este proyecto se dividirá en dos partes, la primera que es la parte comuna es realizada por todos los integrantes del equipo y la segunda, la parte individual la realiza solo el autor de esta tesis. La parte comuna se basa en expandir e incorporar los componentes necesarios en el producto TrustedX on-premise para que pueda funcionar como TrustedX as a Service (TXaaS) y un sistema multi-tenant. Este nuevo producto tendrá la capacidad de cumplir los Reglamentos de eIDAS para ofrecer firmas digitales en el Cloud y tener la misma validez que las firmas notariales manuscritas. La parte individual consiste en crear un prototipo de archivado basado en timestamp utilizando la tecnología Blockchain e integrarlo en TXaaS. Para ello, se estudia el funcionamiento de esta tecnología y las diferentes opciones disponibles en el mercado. Además, se diseña e implementa todos los componentes requeridos para cumplir el objetivo.Technologies advance in leaps and bounds, they mark new trends that emerge to dominate the market, products that were previously novel and nowadays that must be adapted to remain competitive. For this reason, the team, that is made up of 3 students from the FIB - UPC (Arthur Bernal, Marc Méndez and Xiaolei Lin) and is led by the professor and director Francisco Jordan, proposes in this project new innovative technologies that will mark the technology of future and incorporate it into the TrustedX product. This project will be divided into two parts. The first consists of the communal part, which is carried out by all team members and the second, is the individual part that is realized only by the author of this thesis. The common part is based on expanding and incorporating all necessary components in the TrustedX on-premise product in order that it can function as TrustedX as a Service (TXaaS) and a multi-tenant system. This new product will have the ability to comply with eIDAS Regulation to offer digital signatures in the Cloud and have the same validity as the handwritten notarial signatures. The individual part consists of creating a timestamp-based archiving prototype by using Blockchain technology and, integrating it into TXaaS. To fulfill with this, the operation of this technology and the different available options in the market are studied. In addition, all components which are required will be designed and implemented in order to rach with the objective

Assessment of Wind Turbine Aero-Hydro-Servo-Elastic Modelling on the Effects of Mooring Line Tension via Deep Learning

As offshore wind turbines are moving to deeper water depths, mooring systems are becoming more and more significant for floating offshore wind turbines (FOWTs). Mooring line failures could affect power generations of FOWTs and ultimately incur risk to nearby structures. Among different failure mechanics, an excessive mooring line tension is one of the most essential factors contributing to mooring failure. Even advanced sensing offers an effective way of failure detections, but it is still difficult to comprehend why failures happened. Unlike traditional parametric studies that are computational and time-intensive, this paper applies deep learning to investigate the major driven force on the mooring line tension. A number of environmental conditions are considered, ranging from cut in to cut out wind speeds. Before formatting input data into the deep learning model, a FOWT model of dynamics was simulated under pre-defined environmental conditions. Both taut and slack mooring configurations were considered in the current study. Results showed that the most loaded mooring line tension was mainly determined by the surge motion, regardless of mooring line configurations, while the blade and the tower elasticity were less significant in predicting mooring line tension

Short-term Offshore Wind Speed Forecast by Seasonal ARIMA

For maintaining safe operations of wind farms and providing high-quality power supply to the end customers, it is significant to develop reliable short-term time series wind speed forecasting models. In this study, a Seasonal Auto-Regression Integrated Moving Average (SARIMA) model is proposed for predicting hourly-measured wind speeds in the coastal/offshore area of Scotland. The SARIMA model’s performance was further verified and compared with the newly developed deep- learning-based algorithms of Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM). Regardless of the recent development of computational power has triggered more advanced machine learning algorithms, the proposed SARIMA model has shown its outperformance in the accuracy of forecasting future lags of offshore wind speeds along with time series. The comparative study among three predictive models showed that the SARIMA model offered the highest accuracy and robust healthiness

Forecasting of Two-Phase Flow Patterns in Upward Inclined Pipes via Deep Learning

Conventionally, the boundaries of gas-liquid flow regime transition are extremely sensitive to the inclination of flow channels. However, traditional two-dimensional flow regime maps have difficulties to reflect this fact as it can only accommodate two independent variables, which are often the gas and liquid superficial velocities. Few investigators have been able to propose a single model with accessible inputs under the considerations of the whole range of upward inclined angels. In this paper, we developed a novel approach by applying a typical machine learning (ML) method, artificial neural network (ANN), to predict flow pattern along upward inclined pipe (0 ~ 90°) using easily accessible parameters as inputs, namely, superficial velocities of individual phase and inclination angles. TensorFlow, a new generation and popular open-source foundation for ML programming, was used for building the ANN model, which was trained and tested by experimental data (1952 data points) that were reported in the literature. The predicting results show that ANN identifications have a satisfying agreement with experimental observations. The predicting accuracies of stratified smooth, stratified wavy, annular, intermittent, bubble flow are all above 90%, with the only exception of dispersed bubble flow (73%). In addition, the validation of the model was extended by comparing the ANN’s performance with well-established two-phase transition boundary models among different flow regimes. Comparing against conventional methods based on either correlation or flow regime map, the developed ANN model is expected to be a more efficient tool in flow pattern prediction. Furthermore, the impact of inclination angles on final ANN outputs was evaluated quantitatively. Results showed, given flow conditions fixed, variations of inclination angles have a significant influence on gas- liquid flow patterns in channels of conventional sizes

Systematic Investigation of Integrating Small Wind Turbines into Power Supply for Hydrocarbon Production

In this paper, the technical and economic feasibility of integrating SWTs (Small Wind Turbines) into remote oil production sites are investigated. Compared to large turbines in onshore and offshore wind farms, SWTs are more suitable for individual power generations. A comprehensive approach based on wind energy assessment, wind power prediction, and economic analysis is then recommended, to evaluate how, where, and when small wind production recovery is achievable in oilfields. Firstly, wind resource in oilfields is critically assessed based on recorded meteorological data. Then, the wind power potential is numerically tested using specified wind turbines with density-corrected power curves. Later, estimations of annual costs and energy-saving are carried out before and after the installation of SWT via the LCOE (Levelized Cost of Electricity) and the EROI (Energy Return on Investment). The proposed methodology was tested against the Daqing oilfield, which is the largest onshore oilfield in China. The results suggested that over 80% of the original annual costs in oil production could be saved through the integrations between wind energy and oil production

A critical review of wind power forecasting methods - past, present and future

The largest obstacle that suppresses the increase of wind power penetration within the power grid is uncertainties and fluctuations in wind speeds. Therefore, accurate wind power forecasting is a challenging task, which can significantly impact the effective operation of power systems. Wind power forecasting is also vital for planning unit commitment, maintenance scheduling and profit maximisation of power traders. The current development of cost-effective operation and maintenance methods for modern wind turbines benefits from the advancement of effective and accurate wind power forecasting approaches. This paper systematically reviewed the state-of-the-art approaches of wind power forecasting with regard to physical, statistical (time series and artificial neural networks) and hybrid methods, including factors that affect accuracy and computational time in the predictive modelling efforts. Besides, this study provided a guideline for wind power forecasting process screening, allowing the wind turbine/farm operators to identify the most appropriate predictive methods based on time horizons, input features, computational time, error measurements, etc. More specifically, further recommendations for the research community of wind power forecasting were proposed based on reviewed literature

Wind power forecasting of an offshore wind turbine based on high-frequency SCADA data and deep learning neural network

Accurate wind power forecasting is essential for efficient operation and maintenance (O&M) of wind power conversion systems. Offshore wind power predictions are even more challenging due to the multifaceted systems and the harsh environment in which they are operating. In some scenarios, data from Supervisory Control and Data Acquisition (SCADA) systems are used for modern wind turbine power forecasting. In this study, a deep learning neural network was constructed to predict wind power based on a very high-frequency SCADA database with a sampling rate of 1-second. Input features were engineered based on the physical process of offshore wind turbines, while their linear and non-linear correlations were further investigated through Pearson product-moment correlation coefficients and the deep learning algorithm, respectively. Initially, eleven features were used in the predictive model, which are four wind speeds at different heights, three measured pitch angles of each blade, average blade pitch angle, nacelle orientation, yaw error, and ambient temperature. A comparison between different features shown that nacelle orientation, yaw error, and ambient temperature can be reduced in the deep learning model. The simulation results showed that the proposed approach can reduce the computational cost and time in wind power forecasting while retaining high accuracy

Impact of Covid-19 pandemic on electricity demand in the UK based on multivariate time series forecasting with Bidirectional Long Short Term Memory

Due to lockdown measures taken by the UK government during the Coronavirus disease 2019 pandemic, the national electricity demand profile presented a notably different performance. The Coronavirus disease 2019 crisis has provided a unique opportunity to investigate how such a landscape-scale lockdown can influence the national electricity system. However, the impacts of social and economic restrictions on daily electricity demands are still poorly understood. This paper investigated how the UK-wide electricity demand was influenced during the Coronavirus disease 2019 crisis based on multivariate time series forecasting with Bidirectional Long Short Term Memory, to comprehend its correlations with containment measures, weather conditions, and renewable energy supplies. A deep-learning-based predictive model was established for daily electricity demand time series forecasting, which was trained by multiple features, including the number of coronavirus tests (smoothed), wind speed, ambient temperature, biomass, solar & wind power supplies, and historical electricity demand. Besides, the effects of Coronavirus disease 2019 pandemic on the Net-Zero target of 2050 were also studied through an interlinked approach