No abstract available.

Renewable power is one of the main drives to achieve carbon reduction and net-zero, and to meet the ambitious climate goals. In particular, offshore wind power in Europe has been developing at a rapid pace in recent years. Multi-Giga watts offshore wind farms with larger wind turbine power ratings, floating wind turbines installed in deeper water areas, and higher ratio of renewables integrated to existing power grids, are fundamentally changing power system operations and bringing new challenges and technical demands. This industry-doctorate consortium, ADOreD, will recruit and train 15 Researchers by collaborating with 19 academic and industrial organisations. It aims to tackle the academic and technical challenges in the areas of transmission of offshore wind power to the AC grid by using power electronics-based AC/DC technologies. In doing so, it will equip the Researchers, through their PhD studies, with essential knowledge and skills to face fast energy transition in their future careers. The project covers 3 key research aspects: offshore wind (including wind turbines, wind power collection, and wind farm design and control); DC technologies (including AC/DC converters, HVDC control and DC network operation and protection); and AC grid (including stability and control of AC grids dominated with converters under various control modes. The ADOreD consortium has excellent coverage of academic universities and industry organisations including manufacturers, energy utilities, system operators, consultancy and technology innovation centres. All the research questions in the project reflect industry needs; academic novelty and innovation will be reflected in the methodologies and solutions; and the research results will be disseminated directly to the industry partners' products, grid operation and services. The outcomes of the project are both technologies and a talent pool to accelerate the deployment and grid integration of large-scale offshore wind power.

Distributed electricity generation (DG) will play a significant role in future electric power system, as this type of power generation technology can provide electric power by utilising a wide range of renewable energy sources at a site close to end users. Considerable advances have been achieved during past decades in the capacity, scale and location of DG systems, e.g. from onshore to offshore. One of the most critical challenges for the deployment of DG systems relates specifically to availability and reliability in order to sustain energy generation and maximise a long service life of the energy systems unattended. This has, therefore, placed higher demand on predictive maintenance from innovative condition monitoring systems and solutions to tackle new arising challenges in this area. The research proposed in this first grant scheme application represents an effort to explore key issues of generic importance to condition monitoring techniques optimised for fault detection and diagnosis. The research is oriented towards DG systems with wind turbines being the DG sources as this particular application presents a number of realistic challenges. Firstly, measurement signals would exhibit strong non-stationary behaviour due to the intermittent nature of wind sources and fluctuations of grid system. Secondly, the signals of small magnitude may indicate a start of a significant failure, which are normally undetected by conventional methods particularly in a harsh environment. Thirdly, large volume of data needs to be processed and transmitted especially for continuous online monitoring. For example, if we assume that 250 points are required for a typical 2 MW wind turbine to monitor most subsystems of a turbine, this will give rise to 36 million data per day for a 1 GW wind farm under a sampling rate of 5 minutes. Furthermore, a critical issue needing urgent attention will be the health problems of the sensor system, which requires that the monitoring techniques should be assessing what is happening when some of the sensors read data incorrectly. In order to meet such diversified requirements, we plan to use and apply windowed transform, a technique well known for its ability to extract nonstationary components in the measurement data. By the optimal selection of a window shape, automatic windowed wavelet transforms can be achieved to accommodate different sensor data for better feature localisation, extraction and correlation. Although an incipient fault signal is usually of low magnitude and short duration, it would essentially carry the same features as the large ones, such as the regularity. If we can design a suitable algorithm to match the local regularity or singularity of a signal, any incipient faults, abnormalities and disorders can be detected irrespective of their magnitude and time duration. The project is also concerned with designing a hybrid neuro-fuzzy method for optimal sensor data fusion. The use of this artificial intelligence method can best correlate sensor data and predict the unknowns by systematic incorporation of priori information. Minimising the number of sensors whilst still maintaining a sufficient number to assess the system's conditions can not only minimise the complexity of sensor systems but it can also reduce data storage requirements. The final part of the project relates specially to the practical aspect, where the proposed algorithms are validated in real time for online monitoring purposes on a modular embedded system. The proposed condition monitoring system in this project would accommodate all monitoring techniques within one hardware module, which can be readily adapted to other applications. The project will provide better sensing techniques and improved algorithms towards real applications by improving our understanding of how to engineer them in order to aid the decision making process with respect to asset maintenance and management of existing and future DG systems.

The Mission of Supergen Wind 2'To undertake research to achieve an integrated, cost-effective, reliable & available Offshore Wind Power Station.'This will be done under the four objectives:Reliability.Resource estimation.Scaling up of turbine sizes.Lifetime costs.The project will have two parallel Initiating Themes during the first two years. The first to deal with research into the physics and engineering of the offshore wind farm. The second to look more specifically at the wind turbine, building upon the lessons of Supergen Wind 1. In the third and fourth years of the project, the results of these two Themes will feed into a third Gathering Theme, which will consider the wind farm as a power station looking at how the power station should be designed, operated and maintained for optimum reliability and what the overall economics will be.

Energy systems are vitally important to the future of UK industry and society. However, the energy trilemma presents many complex interconnected challenges. Current integrated energy systems modelling and simulation techniques suffer from a series of shortcomings that undermine their ability to develop and inform improved policy and planning decisions, therefore preventing the UK realising huge potential benefits. The current approach is characterised by high level static models which produce answers or predictions that are highly subject to a set of critical simplifying assumptions and therefore cannot be relied upon with a high degree of confidence. They are unable to provide sufficiently accurate or detailed, integrated representations of the physics, engineering, social, spatial temporal or stochastic aspects of real energy systems. They also struggle to generate robust long term plans in the face of uncertainties in commercial and technological developments and the effects of climate change, behavioural dynamics and technological interdependencies. The aim of the Centre for Energy Systems Integration (CESI) is to address this weakness and reduce the risks associated with securing and delivering a fully integrated future energy system for the UK. This will be achieved through the development of a radically different, holistic modelling, simulation and optimisation methodology which makes use of existing high level tools from academic, industry and government networks and couples them with detailed models validated using full scale multi vector demonstration systems. CESI will carry out uncertainty quantification to identify the robust messages which the models are providing about the real world, and to identify where effort on improving models should be focused in order to maximise learning about the real world. This approach, and the associated models and data, will be made available to the energy community and will provide a rigorous underpinning for current integrated energy systems research, so that future energy system planning and policy formulation can be carried out with a greater degree of confidence than is currently possible. CESI is a unique partnership of five research intensive universities and underpinning strategic partner Siemens (contribution value of £7.1m to the centre) The Universities of Newcastle, Durham, Edinburgh, Heriot-Watt and Sussex have a combined RCUK energy portfolio worth over £100m. The centre will have a physical base as Newcastle University which will release space for the centre in the new £60m Urban Sciences Building. This building will contain world-class facilities from which to lead international research into digitally enabled urban sustainability and will also be physically connected to a full scale instrumented multi vector energy system. The building will feature an Urban Observatory, which will collect a diverse set of data from across the city, and a 3D Decision Theatre which will enable real-time data to be analysed, explored and the enable the testing of hypotheses. The main aim of CESI's work is to develop a modular 'plug-n-play' environment in which components of the energy system can be co-simulated and optimised in detail. With no technology considered in isolation, considering sectors as an interlinked whole, the interactions and rebound effects across technologies and users can be examined. The methodology proposed is a system architect concept underpinned by a twin track approach of detailed multi-vector, integrated simulation and optimisation at various scales incorporating uncertainty, coupled with large scale demonstration and experimental facilities in order to test, validate and evaluate solutions and scenarios. A System Architect takes a fully integrated, balanced, long term, transparent approach to energy system planning unfettered by silos and short term thinking.