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On-line Intelligent Diagnostics and Predictive Maintenance Sensor System Integrated within the Wind Turbine Bus-Bar structure to aid Dynamic Maintenance Scheduling

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Improved prediction of wind turbine electrical faults

Wind turbine electrical system failures result in extended downtime, increasing operating costs and decreasing energy security. New on-board sensor technology and predictive tools will enable timely prompt attention to potential problems.

Climate Change and Environment

Wind is one of the most commercially appealing sources of renewable energy, which will help to meet electricity needs and reduce dependence on fossil fuels. The technology is well established and has boasted an annual growth rate of more than 26 % since 1990. However, EU targets of covering 20 % of electricity needs with wind energy within the next two decades require further improvements to enhance reliability and decrease operating costs. Reducing system failures can address both challenges for present and future wind turbines. EU-funded scientist and engineers therefore developed an intelligent sensor network for advanced diagnostics and predictive maintenance of electrical systems. This was achieved within the scope of the WIND TURBARS (On-line intelligent diagnostics and predictive maintenance sensor system integrated within the wind turbine bus-bar structure to aid dynamic maintenance scheduling) project. The consortium successfully developed a model of the electrical components of a wind turbine generator, active rectifier, inverter and output filter. Data modelling of normal operation produced the starting point for evaluation of failure initiation and simulation of key electrical faults identified with a thorough literature review. Project partners also assessed a number of mathematical models and their suitability for identifying signs of degradation and faults. It was shown that degradation can be detected weeks or even months in advance of an actual fault occurring. In addition, models and simulations were used to identify candidate sensors and the selection of initial components. WIND TURBARS technology will enable wind turbine operators to reduce down time of the wind turbine system and therefore enable them to reduce the loss of earnings for electrical production. It will also allow operators to minimise their operational expenditure for repairs by enabling them to exchange components that start to fail early and to conduct preventative maintenance. By addressing wind turbine electrical faults before they affect operation, WIND TURBARS is expected to have huge impact on development of offshore farms given the extreme difficulties associated with inspection and repair. Benefits for onshore and retrofit markets are important as well. Overall, the project stands to make a significant contribution to the EU's renewable energy goals by increasing the energy security afforded by sustainable and eco-friendly wind power.


Wind turbine, downtime, intelligent sensor network, electrical system, WIND TURBARS, diagnostics, mathematical models

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