Large-scale system state estimation with sequential measurements

Incremental state estimation algorithms are tested and compared against the traditional discrete extended Kalman filter for the case of large-scale systems with measurements collected sequentially. Five models from a generic library of power plant component models are used as interconnected sub-models in a large-scale hierarchical model. This test model is simulated with integrated noise, measurement noise and discontinuous inputs. The resultant noisy measurements are collected sequentially rather than as a vector of simultaneous measurements. Several alternative state estimation schemes are implemented and the results are presented and compared