Uncertainty Quantification in State Estimation using the Probabilistic Collocation Method

Conference · Wed Mar 23 04:00:00 EDT 2011

DOI:https://doi.org/10.1109/PSCE.2011.5772599· OSTI ID:1324927

Lin, Guang; Zhou, Ning; Ferryman, Thomas A.; Tuffner, Francis K.

In this study, a new efficient uncertainty quantification technique, probabilistic collocation method (PCM) on sparse grid points is employed to enable the evaluation of uncertainty in state estimation. The PCM allows us to use just a small number of ensembles to quantify the uncertainty in estimating the state variables of power systems. By sparse grid points, the PCM approach can handle large number of uncertain parameters in power systems with relatively lower computational cost, when comparing with classic Monte Carlo (MC) simulations. The algorithm and procedure is outlined and we demonstrate the capability and illustrate the application of PCM on sparse grid points approach on uncertainty quantification in state estimation of the IEEE 14 bus model as an example. MC simulations have also been conducted to verify accuracy of the PCM approach. By comparing the results obtained from MC simulations with PCM results for mean and standard deviation of uncertain parameters, it is evident that the PCM approach is computationally more efficient than MC simulations.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (US)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1324927
• Report Number(s): PNNL-SA-75382
• Country of Publication: United States
• Language: English