This is an online only seminar.
Abstract: Electric power and energy systems are among the most critical infrastructures in modern times; therefore, ensuring a secure and reliable power system operation is of utmost importance. With the increase of variable, intermittent generations, and dynamic loads, power systems operations are becoming crucial and vulnerable to system disturbances, necessitating various special protection systems to exercise fast and adaptive control actions. To address this specific need, Model Predictive Control (MPC), a well-known control strategy for complex processes, is relevant and promising in the power systems context; but considering the complexities of power systems modeling and operations, some fundamental difficulties arise in implementing MPC-based control methodology for real-time operations of power systems. This work presents a machine learning (ML)-accelerated MPC implementation in emergency voltage control problems. The proposed method computes an optimal control strategy for the nominal system model offline and performs successive online control corrections at each control instant to adapt the offline computed controls for real-time scenarios. In the online phase, the required voltage trajectory prediction, and its sensitivity computation to control inputs are achieved by machine learning-based approaches, thereby accelerating the overall control computation multiple times.
Bio: Ramij Raja Hossain received the B.E. degree in electrical engineering from Jadavpur University, Kolkata, India, in 2013. He is currently working toward the Ph.D. degree in electrical engineering with the Department of Electrical and Computer Engineering, Iowa State University, Ames, IA, USA. His research includes data-driven control, distributed optimization, and artificial-intelligence (AI)-based approaches for security, stability, and control of power systems.
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