BEGIN:VCALENDAR METHOD:PUBLISH PRODID:-//Apple Computer\, Inc//iCal 1.0//EN X-WR-CALNAME;VALUE=TEXT:USC VERSION:2.0 BEGIN:VEVENT DESCRIPTION:Speaker: Dr. Guang Lin, Scientist, Pacific Northwest National Laboratory Abstract: Experience suggests that uncertainties often play an important role in controlling the stability of power systems. Therefore, uncertainty needs to be treated as a core element in simulating and dynamic state estimation of power systems. In this talk, a probabilistic collocation method (PCM) will be employed to conduct uncertainty quantification of component level power system models, which can provide an error bar and confidence interval on component level modeling of power systems. Numerical results demonstrate that the PCM approach provides accurate error bar with much less computational cost comparing to classic Monte Carlo (MC) simulations. Additionally, a PCM based ensemble Kalman filter (EKF) will be discussed to conduct real-time fast dynamic state estimation for power systems. Comparing with MC based EKF approach, the proposed PCM based EKF implementation can solve the system of stochastic state equations much more efficient. Moreover, the PCM-EKF approach can sample the generalized polynomial chaos approximation of the stochastic solution with an arbitrarily large number of samples, at virtually no additional computational cost. Hence, the PCM-EKF approach can drastically reduce the sampling errors and achieve a high accuracy at reduced computational cost, compared to the classical MC implementation of EKF. The PCM-EKF based dynamic state estimation is tested on multi-machine system with various random disturbances. Our numerical results demonstrate the validity and performance of the PCM-EKF approach and also indicate the PCM-EFK approach can include the full dynamics of the power sytems and ensure an accurate representation of the changing states in the power systems\n SEQUENCE:5 DTSTART:20101201T140000 LOCATION:KAP 209 DTSTAMP:20101201T140000 SUMMARY:Uncertainty Quantification & Dynamic State Estimation of Power Grid System UID:EC9439B1-FF65-11D6-9973-003065F99D04 DTEND:20101201T150000 END:VEVENT END:VCALENDAR