Open AccessState Estimation of Distribution Network Based on KH–QPSO Optimization Algorithm
Author(s) -
Xiao Fu,
Pei Liang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1650/3/032194
Subject(s) - particle swarm optimization , convergence (economics) , estimation , mathematical optimization , estimation of distribution algorithm , computer science , node (physics) , state (computer science) , nonlinear system , distribution (mathematics) , algorithm , mathematics , engineering , quantum mechanics , physics , systems engineering , structural engineering , economics , economic growth , mathematical analysis
Accurate state estimation is the basis to ensure the normal distribution network operation. To solve the nonlinear optimization problem of distribution state estimation, a state estimation model supply was established by taking the node load value and the output value of distributed power supply as state variables. This paper proposes that Krill herd-quantum behaved particle swarm optimization (KH-QPSO) can avoid premature convergence, and the minimum value is rapidly obtained. In particular, KH and QPSO allow all individuals to obtain truly global optimal solutions without introducing additional operators into the basic KH and QPSO algorithms. An IEEE-33 system is used as a simulation example, which has obvious advantages over other distribution network state estimation models, and the estimation results can provide valuable information for distribution network enterprises and managers.