Power system estimation based on deep learning and system area mapping
Director of Study: Dr. Huilian Liao
Supervisor(s): Dr. Faris Al-Naemi
Additional Information: Open to all students; Self-funded position
Summary
The project is to develop deep learning based power system state estimation approach to estimate different variables and network topology in the power systems with a high accuracy regardless of the uncertainties associated with load demand, DG outputs, fault types, fault area and fault location. The power system estimation therefore can be carried out with the available measurement data only without requiring the information on operating condition at that time. The project also investigates the concept of system area mapping and its use in representing the network configuration. The project will also produce practical guidance for the selection of input feature combination for the problem to be solved.