Abstract
Distribution system state estimation (DSSE) plays a significant role for the system operation management and control. Due to the multiple uncertainties caused by the non-Gaussian measurement noise, inaccurate line parameters, stochastic power outputs of distributed generations (DG), and plug-in electric vehicles (EV) in distribution systems, the existing interval state estimation (ISE) approaches for DSSE provide fairly conservative estimation results. In this paper, a new ISE model is proposed for distribution systems where the multiple uncertainties mentioned above are well considered and accurately established. Moreover, a modified Krawczyk-operator (MKO) in conjunction with interval constraint-propagation (ICP) algorithm is proposed to solve the ISE problem and efficiently provides better estimation results with less conservativeness. Simulation results carried out on the IEEE 33-bus, 69-bus, and 123-bus distribution systems show that the our proposed algorithm can provide tighter upper and lower bounds of state estimation results than the existing approaches such as the ICP, Krawczyk-Moore ICP(KM-ICP), Hansen, and MKO.
Highlights
A new interval state estimation (ISE) algorithm based on the modified Krawczyk-operator (MKO) and interval constraint-propagation (ICP) is proposed for Distribution system state estimation (DSSE)
The simulations are performed on the IEEE 33-bus, 69-bus, and 123-bus distribution systems
An interval state estimation model containing multiple uncertainties consisting of the non-Gaussian measurement noise, the inaccurate line parameters, the stochastic power outputs of distributed generations (DG), and the plug-in electric vehicles (EV) is established
Summary
The measurement model used in distribution system state estimation is given by [2,26]. E m ] T in which z is the measurement vector and x is the state vector. H( x ) is the nonlinear measurement function relating z to x. The weighted least squares (WLS) estimator is widely utilized in DSSE [2]. The state estimation vector xcan be obtained by minimizing the weighted sum of the squares of the measurement residuals. 2 ) is a covariance matrix related to the measurement noise and where R = diag(σ12 , . Where H ( x ) = ∂h( x )/∂x is the Jacobian matrix. Where Π( xk ) = H T ( xk ) R−1 H ( xk ) is the gain matrix. The iterative process will be terminated if the ∆ xk less than a predeterminate tolerance or the iteration step k is larger than a maximum number
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