Power system state estimation via globally convergent methods Export

@article{Pajic2005, abstract = {This paper introduces backtracking and trust region methods into power system state estimation. The traditional Newton (Gauss-Newton) method is not always reliable particularly in the presence of bad data, topological or parameter errors. The motivation was to enhance convergence properties of the state estimator under those conditions, and together with QR factorization to make a globally convergent and reliable algorithm. The trust region formulation shows that such a model is more robust than the traditional Newton (Gauss-Newton) or Backtracking (line search) algorithm. Both algorithms have been programmed and applied to representative power networks, and the computational requirement has been found.}, author = {Pajic, S. and Clements, K. A.}, citeulike-article-id = {967660}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/TPWRS.2005.857383}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1525096}, doi = {10.1109/TPWRS.2005.857383}, journal = {Power Systems, IEEE Transactions on}, keywords = {convergent, estimation, globally, power}, number = {4}, pages = {1683--1689}, posted-at = {2008-07-18 17:11:40}, priority = {2}, title = {Power system state estimation via globally convergent methods}, url = {http://dx.doi.org/10.1109/TPWRS.2005.857383}, volume = {20}, year = {2005} }

TY - JOUR ID - Pajic2005 L3 - citeulike-article-id:967660 N2 - This paper introduces backtracking and trust region methods into power system state estimation. The traditional Newton (Gauss-Newton) method is not always reliable particularly in the presence of bad data, topological or parameter errors. The motivation was to enhance convergence properties of the state estimator under those conditions, and together with QR factorization to make a globally convergent and reliable algorithm. The trust region formulation shows that such a model is more robust than the traditional Newton (Gauss-Newton) or Backtracking (line search) algorithm. Both algorithms have been programmed and applied to representative power networks, and the computational requirement has been found. IS - 4 JF - Power Systems, IEEE Transactions on EP - 1689 TI - Power system state estimation via globally convergent methods VL - 20 SP - 1683 KW - convergent KW - estimation KW - globally KW - power AU - Pajic, S AU - Clements, KA PY - 2005/// UR - http://dx.doi.org/10.1109/TPWRS.2005.857383 ER -