HÀM MỤC TIÊU CHO BÀI TOÁN ƯỚC LƯỢNG TRẠNG THÁI HỆ THỐNG ĐIỆN KHI SỬ DỤNG THUẬT TOÁN TỐI ƯU BẦY ĐÀN

Tóm tắt

The state estimation problem aims to determine the likelihood state of the power system based on the available measurement values. This helps operators to analyze and evaluate the systems so they can make appropriate control decisions. This paper examines six combinations of two algorithms (particle swarm optimization and particle swarm optimization with decoupled variables) and three objective functions (weighted least squares, least absolute values, and weighted least absolute values) to solve the power system state estimation. In addition, rather than employing a penalty function within the objective function as in previous studies, this work use a procedure within the algorithm to verify whether the state variable values remain within the prescribed boundaries. These combinations are simulated for 14-bus and 30-bus IEEE power systems, assuming that input data comes from conventional measuring devices or phasor measurement units. The estimation results show that using the weighted least square function gives the best estimation results