Come meet the poster presenters to ask them questions and discuss their work
Check the programme for our poster viewing moments. For more details on each poster, click on the poster titles to read the abstract. On Wednesday, 6 April at 15:30-16:15, join us on Level 3 of the Conference area for the Poster Awards!
PO249: GIGAN: 3 in 1 sensor of blade deflections, loads, and wind conditions for wind turbines optimization
Eder Murga, Head of New developments and IT, Nabla Wind Hub
Shorter lifespan of wind turbines and frequent downtimes due to mechanical breakdowns are a widespread point of pain in the wind sector. This problem causes great loses and maintenance expenses which increases the cost of wind energy production and jeopardizes profitability. In this context, the accuracy of the data obtained by GIGAN will enable wind farm operators to maximize energy generation and minimize operational risks simultaneously. GIGAN is an innovative combination of optical fiber sensors inside the wind turbine blades and aeroelastic models, which turns the entire turbine into a gigantic anemometer capable of monitoring simultaneously and accurately wind conditions but also the structural loads and fatigue of all its critical parts. Therefore, it will turn every wind turbine into an advanced sensor and monitoring system, to extend turbines’ lifespan up to 40 years. In this sense, GIGAN obtains more accurate and wider information than current solutions at lower cost, allowing to (I) Understand fatigue component per component (from blade tip to foundation) and deploy bankable life extension programs, (II) Increase turbines’ performance up to 4% by reducing yaw misalignments or by unlocking hours of operation per wind direction, and (III) Cut turbine’s OPEX up to a 5% by giving clear roadmap of retrofits and preventive actions in long term. The aeroelastic model included in the system aims to monitor, learn and predict all factors impacting turbine’s performance and lifespan, enabling a long-term optimization of turbine’s performance and minimization of the Levelized Cost of Energy. In conclusion, GIGAN is leading the creation of new knowledge in the area of predictive maintenance of the wind turbines as the project involves a high degree of technical challenge and its contribution to the state of the art is significant.