Calibrating multi-machine power system parameters with the extended Kalman filter

Large-scale renewable resources and novel smart-grid technologies continue to increase the complexity of power systems. As power systems continue to become more complex, accurate modeling for planning and operation becomes a necessity. Inaccurate system models would result in an unreliable assessment of system security conditions and could cause large-scale blackouts. This motivates the need for model parameter calibration, since some or all of the model parameters could either be unknown or inaccurate. In this paper, the extended Kalman filter is used to calibrate the parameters of a multi-machine power system in the presence of faults. The calibration performance is tested under varying fault locations, parameter errors, and measurement noise giving an insight into how many generators and which generators could be difficult to calibrate.