Outlier-Resistant Recursive State Estimation for Renewable-Electricity-Generation-Based Microgrids

In this article, a new state estimation (SE) problem is studied for a class of renewable-electricity-generation-based microgrids with measurement outliers. A state-space system model is proposed for microgrids by resorting to the physical laws of power systems where, in accordance with engineering practice, the prior knowledge of the measurement outliers is unavailable. To enhance the insensitivity against measurement outliers, an outlier-resistant SE algorithm is developed with two distinct features: 1) a saturation function is adopted to constrain the innovation term in the state estimator so as to mitigate the negative influences from the outlier-contaminated measuring data; and 2) an upper bound on the estimation error covariance is first ensured and then minimized by selecting proper gain parameters. Finally, simulation studies with three scenarios are carried out on the benchmark of islanded microgrid which contains two renewable-electricity-generation units to illustrate the validity of the developed outlier-resistant SE algorithm.

Automated summary

This article studies the problem of state estimation in a class of renewable-electricity-generation-based microgrids with measurement outliers. A state-space system model is proposed for microgrids using the physical laws of power systems, without considering prior knowledge of the measurement outliers. To enhance insensitivity against measurement outliers, an outlier-resistant SE algorithm is developed with two distinct features: adopting a saturation function to constrain the innovation term in the state estimator and minimizing the estimation error covariance by selecting proper gain parameters. Simulation studies on a benchmark islanded microgrid with two renewable-electricity-generation units are conducted to illustrate the validity of the developed algorithm.