GOALI: Collaborative Research: A System-Level Framework for Operation and Maintenance: Synergizing Near and Long Term Cares for Wind Turbines Grant uri icon


  • The objective of this Grant Opportunity for Academic Liaison with Industry (GOALI) collaborative research project is to understand how a systems approach to operations and maintenance can enhance the effectiveness and profitability of wind turbines. Currently, operation and maintenance concepts and methods are predominantly components-oriented. This project will show that a system-level approach can yield a better trade-off between risks and costs, thereby optimizing an engineered system for overall economic value. The team will develop a way to measure a wind turbine system''s holistic performance and tracks its decline over time at a system level. Establishing a system-level metric enables us to actively adjust a turbine''s operation and realize a trade-off between near-term power production and system reliability. It also allows the PIs to weigh cost of maintenance against loss in power efficiency, leading to cost-effective decisions of maintenance. The successful development of this new methodology ought to lead to cost-effective operation and maintenance of renewable energy productions, and reduce its overall cost and enhances their marketability (a key to the wind energy''s 20percent-by-2030 goal and long-term sustainability). General Electric, a leading enterprise in wind energy, is a research partner of this project. GE''s direct participation accelerates the application of the resulting methodologies to actual wind energy operational systems. The collaborative nature of this research and industrial involvement brings industrial perspective to the universities and is training our next-generation workforce in the new energy economy. The system-level concepts and methodologies, once demonstrated in wind energy applications, can go beyond and be applied to other power production systems, as well as automobile systems, aerospace systems, and marine systems.

date/time interval

  • 2013 - 2017