Development of Novel Modular Generators
Work Package Leaders; Dr Arwyn Thomas (SGRE), Prof. Zi-Qiang Zhu and Prof. David Stone (UoS EMD)
Challenges: To develop advanced, modular, balanced 3-phase direct-drive (DD) permanent magnet (PM) wind power generator systems, with particular emphasis on improved manufacturability, transportability, reliability and performance.
Background: Next generation (>10MW) large size generators are difficult to manufacture and transport, as their physical size exceeds the present machining and transportation capacities. The proposed modular design will allow generators to be manufactured offsite in a modular format thereby easing manufacturing and transportation. These modules are then assembled on-site. Modularisation of the generator brings a number of associated challenges, such as
(a) inherent 3-phase asymmetries in the design (similar to the case of a linear machine),
(b) double frequency pulsations on the dc voltage ultimately leading to deterioration of the power system quality,
(c) torque pulsations causing noise and vibration of the generator, and
(d) overall reduction in the system reliability and life expectation etc., all to be addressed in the proposal.
The research will focus on researching solutions to these fundamental issues by developing novel modular balanced 3-phase DD PM generator systems including modular generators, converters and control strategies, as well as by performing parasitic effect and sensitivity studies.
Links with other WPs: To address the above key design problems, this WP is multi-disciplinary in nature, involving electromagnetic analysis, electrical machine design, power electronics, control and condition monitoring, which are highly complementary. The results from WP3.5 on generator and converter fault analyses, and health and condition monitoring will also directly affect the performance of modular generator systems.
Potential for future research. The developed concept and techniques for a modular 3-phase wind power generator system can be extended to modular multi-phase generator systems, such as 4, 5, 6, 7 phases etc.
Structural Health Monitoring
of Wind Turbine Blades
Work Package Leaders; Esben Orlowitz (SGRE), Dr Nikolaos Dervilis and Prof. David Wagg (UoS DRG)
Challenges: This WP responds to the industrial drive and academic need to create, for the first time, automatic, online and continuous technologies for damage detection, location, severity assessment and prognosis in offshore wind structures and systems. The need is particularly pressing for the blades, where structural health monitoring (SHM) technology currently remains at an embryonic stage.
Background: WT blades are the most expensive components of a WT, with length that will soon exceed 100m. Current SHM technologies are limited by the fact that monitoring systems are often retro-fitted and make use of sensors already present on the WT for other purposes. The sensor modalities and positions are by no means optimal for SHM. Furthermore, current approaches do not address the fact WT blades are susceptible to multiple modes of failure and therefore need diverse sensor technologies. Finally, the continuous nature of their operation under variable control, loading and environmental conditions makes the employment of a damage detection or prognosis system extremely challenging using current approaches, as these combined effects can mask the presence of damage. The reliability of offshore wind turbines is crucial if they are to be fully integrated into the competitive energy arena; SHM offers a transition to condition-based maintenance, which can lower the LCoE. This WP presents a unique opportunity to develop new data-based and machine learning tools with combined black box and white box models (physics-based models), employed not only for failure detection but also as a method for determining optimal sensor locations. An advanced and robust data-driven approach will be applied in the WP, to give, not only SHM strategies for individual WT components, but also a holistic WT model. The target of this WP is simple and clear, addressing several scientific challenges that are described in detail below. A specific deliverable will be the development of new artificial intelligence algorithms that combine laser technology, evolution of vibration-based and ultrasonic SHM/CM techniques (especially for blades) in full experimental collaboration with Siemens Wind Energy, by monitoring substructures and components using the least possible sensing hardware and online continuous evaluation software.
Links with other WPs: This WP is of core significance to all other WPs, as health monitoring and damage detection is expanded from blades to foundations (WP4), towers and generators (WP1, WP3). The continuous, online health status and failure prognosis of the wind turbine as a whole system involves any major component of the WT in order to ensure that the new generation of offshore WT are reliable. The new technologies that will be developed in this WP will be easily transferrable to the whole WT system.
Potential for future research: The WP focus is to introduce and evolve diagnostic tools that can be used across populations; the ultimate aim being intelligent WTs that communicate with each other by exchanging information and to implement robust SHM/CM systems that learn when new data is coming. This will ultimately lead to less data storage/evaluation and quicker maintenance response times.
Siemens Gamesa Renewable Energy
Siemens Gamesa Renewable Energy
Novel Condition Monitoring and
Fault Detection Techniques and Technologies
Work Package Leaders: Dave Bould (Ørsted), Prof. Simon Hogg (DU).
Challenges: To improve condition monitoring techniques for earlier and more reliable detection, diagnosis and prognosis of emerging faults in wind turbine assets.
Background: The move to far offshore wind farms with the future development of the Round 3 sites in UK waters will result in a step-change in the impact of unplanned maintenance outages on the operational cost of offshore wind energy. Further improvement in condition monitoring techniques for earlier and more reliable detection, diagnosis and prognosis of emerging faults is critically important to the work needed to continually drive down the cost of energy from offshore wind and secure its place in energy systems of the future. The five tasks within this work package will focus on the development and validation of innovative new types of early TRL condition monitoring methodologies for wind energy. Failure analysis of data from both onshore and offshore wind farms has shown that electrical system faults are a common cause of unscheduled turbine outages, whereas major mechanical failures are less common but can result in very long downtime for repairs. The tasks in this work package will investigate new approaches to health monitoring for both mechanical and electrical wind turbine components for these reasons. The work also includes projects aimed at the developing and validating new systems level approaches to fault detection and isolation, based on a holistic consolidation of the condition monitoring data measured throughout wind turbine drive trains.
Links with other WPs: The tasks in WP3 are concerned principally with fault detection on wind turbine drivetrains. The research will link most closely with the modular generator and converter projects grouped under WP1. It is most likely that the greatest interaction will be with tasks 1.1 and 1.3, although there is potential for research engagement with the other tasks in this work package. As WP2 and WP4 concern wind turbine blade and foundations development, there is less scope for interaction with the tasks in these work packages.
Potential for future research: Improving the availability of wind turbines will demand continuous development and improvement of condition monitoring techniques. In addition to further refinement to improve the performance of health monitoring approaches for current generation turbine designs, the development of condition monitoring techniques will also need to keep pace with the innovation and deployment of new turbine component technologies. The tasks contained in this work package will contribute to this effort and it is expected that the outputs from the projects will generate new knowledge that will be applied to other aspect of turbine health monitoring in future follow-on projects.
Investigation of Novel Blade Technology and
Work Package Leaders; Lars Bernhammer (SGRE), Prof. James Gilbert (UoH).
Challenges: To establish advanced modelling, design and manufacturing techniques for the development and evaluation of novel blade and foundation structures and embedded sensing systems capable of monitoring manufacturing processes and operational loading.
Background: The drive to reduce the LCoE and increase the range of locations where OSW can be applied will require the adoption of larger turbines and innovative foundation systems. Current blade design, material and manufacturing methods are approaching their limits and new approaches will be required. More sophisticated blade structures will be required to give improved mechanical performance with reduced weight and material use. Understanding the thermal and mechanical stresses experienced through manufacture, transport, installation and operation of blades using embedded sensors will allow better lifetime prediction and improve future design iterations, reducing over-design. There is an opportunity to embed fibre optic sensors into blades which can be used to monitor both manufacturing process parameters and provide a rich new stream of data for structural health and condition monitoring. Larger turbines and floating platforms require better understanding of seabed soil structure and the behaviour of novel foundations. Current models do not provide sufficient fidelity for reliable predictions, and so new constitutive models focussed on appropriate seabed soil structures will be developed and validated. These models, will be applied to the design and optimisation of novel anchors which potentially provide more cost effective solutions with lower environmental impact.
Links with other WPs: The improved understanding of blade stresses developed through WP4.1 will help inform sensor placement in WP 2.2, 2.3 and 2.5. WP 4.2 will use data about optimum sensor placement derived in WP2.3 and will in turn provide data for SHM techniques developed in WP2.3. It may also generate data for use in WP2.2. and 2.5. WP 4.3 and 4.4 will be closely linked providing generic models which can then be applied and evaluated in specific applications.
Potential for future research: The improved understanding and modelling of blade manufacturing and structural performance developed through this work package has the potential to facilitate next generation blade designs with more radical structures produced using novel materials and manufacturing processes. Likewise, the improved understand of seabed-anchor interactions will reduce the development time and risk associated with new bottom fixed and floating offshore turbine structures.
Siemens Gamesa Renewable Energy
Siemens Gamesa Renewable Energy