Effective dynamic state estimation algorithm for Islanded microgrid structures based on singular perturbation theory

This paper introduces an effective dynamic state estimator for Islanded Microgrids. Basing on a set of nonlinear Differential Algebraic equations representing the electrical grid and the energy sources, the Singular Perturbation Theory is used to obtain a modified mathematical representation of the Microgrid model to develop an effective dynamic state estimator based on the Unscented Kalman Filter. It is shown that Singular Perturbation Theory is a viable tool that permits the design of a dynamic estimator able to effectively recover the steady-state and dynamic states of the electrical grid, that is, the nodal voltages and the dynamic variables of generator units. Furthermore, the Microgrid state is suitably recovered using fewer measurements than those needed by conventional static estimators. The performance of the proposed scheme is evaluated using a practical Microgrid containing wind power and hydroelectric generators, under load and wind variations as well as three-phase faults. Also, this timely approach is compared with the Extended Kalman Filter for Differential Algebraic systems, demonstrating the superior effectiveness of the developed state estimator: the errors obtained by the new dynamic state estimator are 80% smaller than those obtained by the conventional Extended Kalman Filter, for the same applied noises. Moreover, a comparative study case with the Unscented Kalman Filter is included.