ROBUST STATE ESTIMATION METHOD OF DISTRIBUTION NETWORK FOR STOCHASTIC VARIABLESWITH DYNAMIC INTERVAL VALUES

The uncertainties in measurement make the estimation accuracy of traditional methods difficult to meet the dispatching requirements of distribution systems. At the same time, the measurement system often has bad data during the acquisition and transmission process, which seriously affects the accuracy of state estimation. The method in this paper is used to improve the influence of the state estimation on measurement uncertainty and bad data, and improve the robustness and accuracy of estimation. Interval analysis method is used to describe the measurement problem with uncertainties, the interval constraint model of state variables in distribution network is established, and the feasible region of state variables is obtained using linear programming method. Based on the measurement uncertainty theory, a robust state estimation optimization model with the highest measurement point accuracy as the objective function is established. The precise value of the state estimation is solved by the interior point method. With the feasible region of state variables as constraints and the median value of the interval as the initial value, it is unnecessary to take the calculation results of power flow as the initial value, thus reducing the scope of solving state variables and reducing the amount of calculation. Compared with the traditional weighted least square, this method has a significant improvement in accuracy and resistance. The feasibility and effectiveness of this method are verified by IEEE30 and IEEE118 systems.