Decentralized robust state estimation of multimachine power systems

Abstract The problem of online estimating the states of nonlinear (NL) Multimachine Power Systems (MPSs) is addressed within a constructive framework that combines notions and tools from electrical engineering and nonlinear estimation theory. First, the standard NL centralized model is realized as a set of linear decentralized robustly observable models, with augmented states that capture nonlinearity, parameter error, and intermachine state interaction. Then, based on the observability property of the decentralized model, a robustly convergent linear Geometric (Luenberger-like with integral action) estimator with simple tuning is constructed. With respect to previous MPS estimation techniques, the novelties are: (i) from a theoretical perspective, the comprehensiveness of the methodology, with model design, solvability in terms of observability, and robust functioning criteria coupled with a simple tuning scheme, and (ii) from an industrial applicability viewpoint, an online computation load considerably smaller than the one of the NL Extended Kalman Filter (EKF), and a tuning scheme appreciably simpler than the one of the NL sliding mode perturbation observer (SMPO). The proposed design methodology is illustrated through numerical simulation with a representative case example.