Dynamic model assisted fault diagnostics of wind turbine gearbox. This project aims to develop novel condition monitoring methodologies for the gearbox of large horizontal-axis wind turbines which are widely installed in wind farms for generating renewable energy. This project expects to generate a new diagnostic framework by integrating dynamic model assisted simulations and digital twin-based approaches. Expected outcomes of this project include new vibration-based methods for fault diagnostic ....Dynamic model assisted fault diagnostics of wind turbine gearbox. This project aims to develop novel condition monitoring methodologies for the gearbox of large horizontal-axis wind turbines which are widely installed in wind farms for generating renewable energy. This project expects to generate a new diagnostic framework by integrating dynamic model assisted simulations and digital twin-based approaches. Expected outcomes of this project include new vibration-based methods for fault diagnostics and predictions of the remaining useful life of turbine gearboxes. This should provide significant benefits to the Australian Wind Industry by ensuring reliable operation of wind turbines, reducing turbine downtime and reducing operation and maintenance costs; ultimately lowering the cost of energy from wind.Read moreRead less
Harnessing the Power of Wind: Revolutionising Wind Farm Optimisation. This project aims to develop a rigorous, efficient and accurate framework for optimisation of control policies for complete wind farms. It expects to generate new knowledge in data-driven physics informed transient aerodynamic and structural modelling of entire wind farms, generation of low order yet sufficiently accurate models using machine learning, and game-theoretic and model predictive control techniques for operation of ....Harnessing the Power of Wind: Revolutionising Wind Farm Optimisation. This project aims to develop a rigorous, efficient and accurate framework for optimisation of control policies for complete wind farms. It expects to generate new knowledge in data-driven physics informed transient aerodynamic and structural modelling of entire wind farms, generation of low order yet sufficiently accurate models using machine learning, and game-theoretic and model predictive control techniques for operation of an entire wind farm. Expected outcomes are engineering tools to tackle wind farm inefficiencies totalling $700m/year in Australia alone, contributing to energy stability, security and lowered emissions aligned to the National Science and Research Priority ‘Energy’.Read moreRead less