State Estimation for Heavily Loaded System: A Comparative Study

Real time state information provided by the state estimator plays a major role in power system monitoring and control. As a result, the convergence of the estimator under various system operating conditions becomes one of the key requirements. This paper presents a robust state estimation framework that generalizes several well-known estimators. The statistical robustness of each estimator has been studied analytically through the total influence function. Furthermore, the correlation between the statistical robustness of an estimator and the numerical robustness of the iterative algorithm is investigated as well. Numerical results carried out on the IEEE test system reveal that the Schweppe-type Huber generalized maximum-likelihood estimator works well in all simulated scenarios while the other alternatives have convergence problems or numerical instability issues under stressed system operating conditions.