Dr. Breiffni Fitzgerald

In the current state-of-the-art, each turbine in a wind farm is controlled to maximise its own individual performance, ignoring the effect that the turbine has on the other turbines in the wind farm - this is a sub-optimal approach. MeLodiC will develop new wind farm models taking aerodynamic wake interaction effects into account. These new models will be used to develop novel wind farm controllers with the goal of holistically optimizing wind farm power production. Advanced control algorithms will be integrated with powerful machine learning techniques (using high performance computing) to adaptively 'learn' the best way to control wind farms.

NexSys targets the development of new multi energy system models across a variety of sectors. The Energy system represented by the electricity and gas infrastructure are central components to be modelled on a variety of spatial and temporal scales. The integrated energy system models will consider the supporting strands of Water, Transport, Offshore Wind and Cities and Communities. Each of which will both develop research within their own domains and link directly into the Energy System. Mathematical modelling and optimisation are at the core of NexSys aiming to reveal insights into the nature and behaviour of energy infrastructure with a transdisciplinary focus targeting the highly relevant cross-cutting themes of climate, data, society, and finance. Pathways towards the achievement of net Zero carbon energy system underpinned by detailed physical modelling accompanied by the development of new technologies in each domain will be critical aim of the programme.

TwinFarm will utilize data driven modelling, real-time monitoring techniques, and the fundamental physics governing wind farms to develop digital twins of wind farms. The TwinFarm research programme will progress the state-of-the-art by developing realistic data-driven wind farm models accounting for turbulent interacting wakes and coupling these with real-time monitoring approaches to balance the loading across a farm and thus improve availability and fatigue life. Using our approach we will achieve realistic and close to real-world outputs from our models without the computational effort of full dynamics models. This will allow our wind farm models to run in real-time and will provide wind farms with a powerful tool for wind farm monitoring, maintenance and management. TwinFarm's novel unified, holistic methodology will improve wind farm performance, prevent costly failures/downtime and predict fatigue loads across an operational wind farm. This is a low cost, low risk and high reward project that can provide impact for industry, particularly the Irish wind farm sector which relies on aging wind turbine infrastructure. The TwinFarm research programme would complement the work envisaged in the SEAI defined topics 7 (End of life wind turbines: Life extension, decommissioning or repowering) and 15 (Remote and autonomous inspection and maintenance of onshore and offshore wind turbines) for a small additional cost. Successful completion of the project would essentially lead to a software upgrade for the existing fleet of Irish wind farms that would be very helpful for the industry and the country at a crucial time in our renewable energy journey.

Wind turbines are normally designed for 20 years lifetime. In Ireland, there will be a significant number of onshore wind turbines reaching their end-of-life (500 turbines by 2025 and 1000 by 2030). There are generally three options for wind plants at end-of-life: life extension, decommissioning and repowering. This project will develop a comprehensive decision-making tool for end-of-life wind turbines in Ireland through life assessment for the critical components of wind turbines including foundation, tower and blades. A holistic structural dynamic models of wind turbines will be developed using a multi-body approach which accounts for the aeroelastic behaviour of the wind turbines employing Irish meteorological conditions. Cutting-edge structural health monitoring techniques will be developed for lifetime extension of wind turbines. Novel approaches will be developed for anomaly detection and fatigue assessment of the tower structure through smart advanced identification methods. In addition, a new methodology will be developed for predicting remaining fatigue life and residual strength of wind turbines' blades periodically until their end-of-life. Lifecycle assessment of foundations will be developed which will provide a set of recommendation outlining options for the foundation to include remaining operational life. Furthermore, a range of energetic, environmental, social and economic factors will be considered in order to arrive at optimal solutions for sustainable decommissioning of end-of-life wind turbines in Ireland. Finally, a new protocol will be developed for repurposing decommissioned blades for specific purposes. The results will form the most authoritative evidence base around the topic for Ireland and will be an international benchmark.

REMOTE-WIND uses instrumented air and water-borne drones, with non-contact measurement through short-wave infra-red multi-point Laser Doppler Vibrometry (SWIR-LDV) for bespoke autonomous inspection and monitoring of onshore/offshore wind turbines to assess their lifetime safety and serviceability performance and take better decisions around maintenance, repowering and decommissioning. It creates a first benchmark of its kind, aligned to current wind energy targets and future aspirations of Ireland. The project assimilates fundamental physics, sensors, robotics and cutting-edge analytics to create scientific insights and translates them to technology and commercial practice. REMOTE-WIND will carry out full-scale demonstrations for onshore and offshore farms, venturing significantly beyond laboratory tests and small academic exercises. It will create extensive guidelines and recommendations, along with a suite of software tools for industrial adoption these solutions for their needs, allowing for the evolution of these tools with changing performance demands of future wind turbines. The results from this project will de-risk the wind energy sector, lower Operations and Maintenance costs leading to improvement in Levelized Cost of Electricity and improve Health and Safety of inspection workers. It will also create critical and demonstrative evidence base for such monitoring to be commercially carried out in Ireland. REMOTE-WIND will be transformative in terms of how maintenance is carried out for these turbines and positively impact their annual maintenance contract terms, leading to better support of Irish businesses. It will establish monitoring at the core of renewable energy policy and provide Irish thought leadership globally in this niche and strongly bourgeoning sector.