Wind Turbine and Farm Control via Reinforcement Learning

Abstract

Wind power plays a vital role in the global effort toward net zero. The control system is the core of wind turbine and farm operations and has essential influences on the power capture efficiency and maintenance cost. Substantial efforts have been made to investigate the control methods of wind turbines and farms. However, there still exists a research gap due to the complex and nonlinear systems of modern wind turbines & farms. Reinforcement learning is a cutting-edge intelligent technique that can handle high-complexity and nonlinearity control problems. Therefore, this seminar aims to introduce RL-based wind turbine and farm control technologies to increase power generation, reduce loads, and achieve optimal control performance in the presence of uncertainties and faults.

Speaker

Under the support of the Chancellor’s International Scholarship, Ms Jingjie Xie pursued her PhD degree in Control Engineering at the School of Engineering, University of Warwick, United Kingdom. Ms Xie received her B.S. degree in information engineering from Northwestern Polytechnical University in 2016 and her M.S. degree in Control Science and Engineering from Beihang University in 2019. She has published several leading journal papers in the Control & Aerospace fields. Her current research interests include Reinforcement Learning, Model Predictive Control, and their applications in the Aerospace Community.