Dual Set-Membership State Estimation for Power Distribution Networks Under Event-Triggered Mechanism

This article is concerned with the set-membership state estimation problem for power distribution networks (PDNs) over a resource-constrained communication network under influence of unknown but bounded (UBB) noises. Firstly, in order to alleviate pressure information (ICN) while meeting requirements, event-triggered mechanism adopted send data containing more valid center, reasonably reducing signal transmission frequency. Secondly, an dual filter (ET-DSMF) designed improve performance estimation. The proposed performs discrete approximation semi-infinite programming by random sampling technique, and compact linearization error bounding ellipsoid obtained solving nonlinear programming. Subsequently, sufficient condition existence estimated derived depending on mathematical induction method. key time-varying gain matrix optimal are determined recursively convex optimization problem, according minimum trace criterion. Finally, effectiveness filtering algorithm demonstrated performing simulation experiments IEEE 13 test system.