The power output from wind turbines has increased dramatically over the past thirty years from 50 kW to 6 MW, while 8-12 MW turbines are in the stage of design. State-of-the-art condition monitoring systems, such as vibration-based systems and temperature sensors, are able to monitor and evaluate the current condition of components of interest. Nonetheless, varying wind loads can result in the generation of false alarms or even misinterpretation of the data collected. In addition, commercially available condition monitoring systems offer no or very limited prognostics capability with regards to the remaining lifetime of a component before a serious fault occurs. Therefore evolution to predictive maintenance strategies is currently impossible. Experience has shown that by combining disparate data sources wind farm operators will be able to move from common reactive maintenance approach to a more cost effective risk-based operation and maintenance strategy with a high level of predictive maintenance scheduling. OPTIMUS will develop and demonstrate in the field novel methods and tools for prognosis of the remaining lifetime of key components based on data acquired by a cost-effective wind turbine condition monitoring system implemented by custom-designed dependable computing systems. This technology will reduce the total cost of energy and advance the deployment of large scale offshore and onshore wind energy by increasing availability and reducing downtime due to unplanned maintenance. Predictive maintenance will also reduce costs incurred from secondary damage to components and enable maintenance activities (and the associated costs) to be optimized with respect to forecast revenue from power generation. The results of this project will lead to a significant step-change over the current capability of commercial condition monitoring systems.
Fields of science
Call for proposal
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Funding SchemeCP - Collaborative project (generic)
M3 2WJ Manchester
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