Interior Point Method For Power System Weighted Nonlinear L, Norm State And Parameter Estimation Research

State estimators (SE) are the heart of modern Energy Management System (EMS). The performance of any other application program strongly depends on the accuracy of data provided by the SE. Transformer tap position changes frequently in power systems. If control center doesn't receive correct network parameters, SE can't get the real network states.On the basis of the Perturbed Karush-Kuhn-Tucker (KKT ) conditions of the primal problem, this paper presents a new interior point algorithm to solve power system weighted nonlinear L( norm (IPWNL1) state and parameter estimation problem. This algorithm improves confidence in SE by estimating parameters and states at the same time.Simulation results on test power systems which range in size from 4 to 118 buses, have shown the virtues as follows: getting unbiased estimationwithout detecting and identifying bad data in measurements; solving state and parameter estimation for power system with good convergence and excellent robust property; increasing the numbers of iterations a little bit with the test systems expanded; estimating many transformer taps simultaneously and remaining the main state estimation; keeping the estimated relative error within + 0.1% and processing efficiently equality constraints and ill condition with polynomial complexity. In addition, fill-ins are greatly reduced so as to enhance the computational efficiency with novel data structure.