Current Injection Methods for Hybrid State Estimation

This paper presents current injection methods for robust state estimation with hybrid measurements. It employs the perturbed Gauss-Newton method to extend Weighted LeastAbsolute Value (WLAV) state estimation from networks measured only by phasor measurement units to hybrid measured networks. The resulting complex variable Iteratively Re-weighted Least Squares (IRLS) method can efficiently yield a WLAV-type solution. It thus carries the efficiency benefits of processing a reduced-size linear system that does not have the conjugates of complex-valued measurements and phasor voltages, in addition to measurement scaling for superior bad data rejection. Numerical results are reported on networks with up to 82000 nodes, and a comparative analysis is carried out against an interior-point solver for formulations with conic constraints (that are equivalent to the complex variable IRLS implementation) and absolute value representations (that are equivalent to the real variable IRLS implementation). The complex variable IRLS implementation has the potential to significantly improve state estimation because it is more computationally efficient than the real variable one and is also better at dealing with bad data by inherently exploiting measurement dependencies. An average speedup factor of approximately three is also observed compared to a classical Wirtinger implementation of the IRLS estimator.