Sensitivity analysis of the Kalman Filter and its applications in power systems

Power system model integrity is essential to many planning and operation tasks to ensure the safety and reliability of electricity delivery. Inaccurate system models would result in unreliable assessment of system security conditions and cause large-scale blackouts such as the 2003 Northeast Blackout. This dictates a strong need for knowing the sensitivity of the model to errors in certain input variables. Our previous work has demonstrated the feasibility of applying the Extended Kalman Filter (EKF) to dynamically monitor generator parameters using disturbance data recorded by phasor measurement units (PMU). This paper uses simulation to examine the effectiveness of augmenting the state variables with the uncertain input variables to improve state variable tracking and to calibrate the input variables.