Robust Parameter Estimation of the French Power System Using Field Data

This paper proposes a robust parameter estimator to deal with the problem of inaccurate power losses calculated from the estimated state variables. These power loss inaccuracies result from some incorrect branch impedance due to non-stationary ambient conditions or geometrical constraints unreported in the database. Specifically, we use the projection statistics and the coupling relationship between parameter errors and the measurements to detect incorrect branch parameters associated with leverage points, which are proved to show the largest influence on the state estimates. Then these suspicious parameters are augmented with the original system state variables for the generalized estimation. Finally, the generalized robust estimator is executed as a nonlinear constrained optimization problem. The necessary condition for the primal-dual interior point method-based solver is derived. Our proposed method is able to enforce each suspicious parameter within its validity domain expressed as inequality constraints. Thanks to the robustness of the proposed Schweppe-type Huber (SHGM)-estimator and the enhanced measurement redundancy using multiple night off-peak snapshots of a nearly constant power system state, incorrect parameter values and erroneous zero injections are reliably detected and effectively corrected and validated. The performance of the weighted least squares estimator, the generalized least absolute value estimator, and the SHGM-estimator are evaluated and compared with regards to the estimated values of impedances and losses. Field test results on the realistic 2000-bus French power system show that the SHGM-estimator outperforms the other two estimators.