Open AccessA SCADA/PMU hybrid measurement state estimation method considering load uncertainty
Author(s) -
Tianen Huang,
Hong Sun,
Zhenjie Wu,
Xingchen Zong,
Yuxiao Li,
Yuantao Wang,
Jian Tang,
Xiang Li,
Yajun Mo,
Chengda Li,
Shuangdie Xu
Publication year - 2022
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/983/1/012008
Subject(s) - scada , monte carlo method , electric power system , measurement uncertainty , estimation , state (computer science) , control theory (sociology) , computer science , power (physics) , observational error , reliability engineering , engineering , control (management) , algorithm , statistics , mathematics , physics , systems engineering , quantum mechanics , artificial intelligence , electrical engineering
This paper proposes a power system state estimation method combining the Monte Carlo method and the SCADA/PMU hybrid measurement state estimation algorithm. Quantify the impact of load uncertainty on the state estimation results to reduce the influence of power system measurement error. The results show that the method takes into account the uncertainty of load measurement and the influence of flexible load, and accurately estimate the operating state of the power system.