• Energy Research

The autonomous marine vehicles development has a grow demand by offshore industries. The multiple facilities implemented in this environment requires the evolution of this devices for several task as survey, maintenance or monitorization. The technology complexity of this devices requires great efforts in economics and resources to innovate. In this line, the ENDURUNS project purposes to design an autonomous marine system capable to performance a long endurance during the missions due to the employment of renewable energies for its vehicles. The evaluation of the project life cycle represents an important task for the project management. In this article, it is exposed the three different project life cycle aspects. The aspect analyzed are the social life cycle assessment, the life cycle cost and the life cycle assessment. It has been applied the corresponding standards in the European context to develop each methodology and to obtain the results. Thus, this work brings an approach about the environment, economic and social impact of this project. The results presented from this study can be considered for practitioners for future research in marine mobility field, due to the sustainability characteristics of the project analyzed.

Wind energy is currently the most widely implemented renewable energy source in global scale. Complex industrial multi-MW wind turbines are continuously being installed both onshore and offshore. Projects involving utility-scale wind turbines require optimisation of reliability, availability, maintainability and safety, in order to guarantee the financial viability of large scale wind energy projects, particularly offshore, in the forthcoming years. For this reason, critical wind turbine components must be identified and monito red as cost-effectively, reliably and efficiently as possible. The condition of industrial wind turbines can be qualitatively evaluated through the Fault Tree Analysis (FTA). The quantitative analysis requires high computational cost. In this paper, the Binary Decision Diagram (BDD) method is proposed for reducing this computational cost. In order to optimise the BDD a set of ranking methods of events has been considered; Level, Top-Down-Left-Right, AND, Depth First Search and Breadth-First Search. A quantitative analysis approach in order to find a general solution of a Fault Tree (FT) is presented. An illustrative case study of a FT of a wind turbine based on different research studies has been developed. Finally, this FT has been solved dynamically through the BDD approach in order to highlight the identification of the critical components of the wind turbine under different conditions, employing the following heuristic methods: Birnbaum, Criticality, Structural and Fussell-Vesely. The results provided by this methodology allow the performance of novel maintenance planning from a quantitative point of view.

Abstract Grouted connections for offshore wind turbines are formed by attaching overlapping steel piles with an ultra-high strength cementitious grout. The structural performance of grouted connections is critical for the substructures in order to exhibit sufficient resistance to environmental loads. The long-term integrity of the grout core can be compromised due to the complex stress states present, leading to unexpected slippage and gaps in the steel-grout interface, grout cracking and water ingress. This paper presents the results of an experimental investigation on damage evolution and failure mechanisms occurring within grouted connections in laboratory-based bending tests using acoustic emission. A parametric analysis of the detected acoustic emission signals has been conducted. The acoustic emission activity has been correlated with load-displacement measurements and the observed specimen failure modes. For the tested grouted connections, the number of acoustic emission hits and the signal duration were employed to identify damage evolution during load application. Root mean square and the ratio of rise time to amplitude were found to be useful Key Performance Indicators (KPIs) for damage prognosis. Finally, an improved b-value analysis has been performed, and the computed drops were well-associated with grout cracking within the connection.

Against the background of steadily increasing wind power generation worldwide, wind turbine manufacturers are continuing to develop a range of configurations with different combinations of pitch control, rotor speeds, gearboxes, generators and converters. This paper categorizes the main designs, focusing on their reliability by bringing together and comparing data from a selection of major studies in the literature. These are not particularly consistent but plotting failure rates against hours lost per failure reveals that problems with blades and gearboxes tend to lead to the greatest downtimes. New, larger wind turbines tend to fail more frequently than smaller ones so condition monitoring will become increasingly necessary if levels of reliability are to be improved.

Wind Turbines (WT) are one of the fastest growing sources of power production in the world today and there is a constant need to reduce the costs of operating and maintaining them. Condition monitoring (CM) is a tool commonly employed for the early detection of faults/failures so as to minimise downtime and maximize productivity. This paper provides a review of the state-of-the-art in the CM of wind turbines, describing the different maintenance strategies, CM techniques and methods, and highlighting in a table the various combinations of these that have been reported in the literature. Future research opportunities in fault diagnostics are identified using a qualitative fault tree analysis.

Condition monitoring of wind turbine gearboxes has mainly relied upon vibration, oil analysis and temperature monitoring. However, these techniques are not well suited for detecting early stage damage. Acoustic emission is gaining ground as a complementary condition monitoring technique as it offers earlier fault detection capability compared with other more established techniques. The objective of early fault detection in wind turbine gearboxes is to avoid unexpected catastrophic breakdowns, thereby reducing maintenance costs and increase safety. The aim of this investigation is to present an experimental study the impact of operational conditions (load and torque) in the acoustic emission activity generated within the wind turbine gearbox. The acoustic emission signature for a healthy wind turbine gearbox was obtained as a function of torque and power output, for the full range of operational conditions. Envelope analysis was applied to the acoustic emission signals to investigate repetitive patterns and correlate them with specific gearbox components. The analysis methodology presented herewith can be used for the reliable assessment of wind turbine gearbox subcomponents using acoustic emission.

Abstract Renewable energy sources have a key role to play in the global energy mix as a means of reducing the impact of energy production on climate change. Wind energy is the most developed of all renewable energy technologies with more than 200 GW of globally installed capacity as of 2011. Analyses of wind farm maintenance costs show that up to 40% of the outlay is related to unexpected component failures that lead to costly unscheduled amendments. Wind farm operators are constantly looking for new technological developments in condition monitoring that can contribute to the minimisation of wind turbine maintenance expenditure. Early fault detection through condition monitoring can help prevent major breakdowns as well as significantly decrease associated costs. Moreover it enables the optimisation of maintenance schedules, reduces downtime, increases asset availability and enhances safety and operational reliability. Faults in the braking system are of particularly concern since they can result in catastrophic failure of the wind turbine. The present study investigates online condition monitoring based on voltages and currents for mechanical wind turbine brake system fault diagnosis.