مشخصات پژوهش

Distribution system state estimation plays a crucial role in supplying essential data for system monitoring and control. However, the presence of uncertain parameters such as the variable output of distributed generation (DG), random meter errors, and inaccurate network parameters poses a significant challenge to achieving situational awareness (SA) in distribution systems. To address these challenges, this study introduces an innovative two-stage stochastic programming model. In the first stage, the model focuses on the optimal placement of μ-PMUs with the objective of minimizing installation costs, maximizing measurement redundancy, and enhancing system observability. This is particularly important in the context of partially zero injection nodes (PZIN). In the second stage, the model performs state estimation for three-phase asymmetric DG-integrated distribution systems, aiming to improve SA. The application of the proposed model resulted in the identification of optimal μ-PMU locations in the presence of PZINs and various contingencies. The distribution system state estimation achieved high accuracy and a low error percentage. The feasibility and effectiveness of this methodology were validated using the modified IEEE 85-bus distribution system. In addition, we incorporated extended Kalman filter (EKF) state estimation to compare with the weighted least square method.