Regularized Learning Enabled Monitoring and Control for Wind Power Systems

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The objective of this project is to develop new monitoring and control strategies for enhancing wind turbine reliability so that operations and maintenance costs of wind energy can be reduced. In wind power systems, the "wind input"-to-"turbine response" relationship is nonstationary, due to both internal (e.g., system's degradation) and external (e.g., surface contamination on blades) changes. This nonstationary dependency causes significant technological challenges in managing the health and performance of wind turbines. This project will develop a new regularized learning method to characterize the time-varying dependency among system variables so that changes in a turbine system can be tracked and predicted. Subsequently, a statistical monitoring method with adaptive control limits will be devised to signal the occurrence of anomalies. Based on the results from the regularized learning process, an adaptive control strategy will be developed to mitigate excessive and undesired mechanical stresses on turbine subsystems in an effort to prevent or slow the deterioration process. A new wireless structural health monitoring (SHM) system supporting real-time, embedded data processing will be advanced to track the behavior, performance and health of operational turbines. The outcomes of this research will facilitate the wind industry's smooth transition from using rudimentary diagnosis and control techniques to the use of sophisticated and integrative monitoring and control technologies. The new monitoring method will enable timely detection of anomalies while reducing the false alarms. Optimally determined control parameters will balance between power production and stress levels in an effort to extend a turbine?s service life. While using wind turbines as the primary application target, the methodology is applicable to other engineering systems subject to dynamic operating conditions including civil infrastructure systems. This project will contribute toward the preparation of a future workforce in the field of renewable energy and sustainability through an array of mechanisms including the integration of under-represented students in the STEM field into renewable energy research, opportunities for students to interact with national laboratories, and to be engaged with other domestic and international research groups.