Open AccessRevised constraint‐propagation method for distribution interval state estimation
Author(s) -
Ngo VietCuong,
Wu Wenchuan,
Lou Yining
Publication year - 2020
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2019.1679
Subject(s) - interval (graph theory) , observability , interval arithmetic , interval estimation , constraint (computer aided design) , mathematics , state (computer science) , prediction interval , algorithm , mathematical optimization , statistics , computer science , confidence interval , geometry , combinatorics , mathematical analysis , bounded function
State estimation affords real‐time network modelling facilitating distribution network (DN) operation and control, and is an indispensable component of distribution management systems. However, given the lack of real‐time measurements of DN, its observability relies heavily on pseudo‐measurements, which are associated with relatively large errors. So the pseudo‐measurements can be expressed as interval numbers. In such cases, interval state estimation models maybe useful. Using interval analysis methods, interval state estimations yield the upper and lower bounds of system state variables, but such conventional methods ignore correlations among interval numbers; the estimations are very conservative. Here, the authors develop an improved interval analysis method by combining the interval constraint‐propagation (ICP) algorithm with the Krawczyk–Moore test. Numerical tests of IEEE distribution systems at different scales showed that their method outperformed conventional ICP methods.