Interval State Estimation of Distribution Network With Power Flow Constraint - Zhi Wu; Huiyu Zhan; Wei Gu; Shujiang Zheng; Bojiang Li (IEEE Access, 2018)
Currently, distribution network is faced with many problems, e.g., low automation coverage and less data acquisition. There are also lots of challenges in state estimation, such as imprecise approximation of network parameters and measurement devices as well as integration of distributed generations. In order to deal with these problems of uncertainties in distribution network, an interval state estimation with power flow constraint is proposed in this paper, which is based on the quantitative description of the uncertain parameters, distributed generations, and system measurements with interval numbers. Given the hybrid measurement data, an interval linear state estimation model is established. In order to estimate state values precisely, an iterative Krawczyk algorithm is proposed to optimize this model. Furthermore, power flow constraint is introduced into the original equations of the interval state estimation model to improve the computation speed and accuracy. Modified IEEE 57-bus system is used to verify the effectiveness of the proposed method. Taking the results of Monte Carlo simulation as actual values, the proposed method performs better both in convergence and estimation accuracy compared with the existing unconstrained interval solving method.