刘怡君,杨洪耕,王佳兴,王泽.基于量子行为粒子群优化算法的电压暂降状态估计[J].电测与仪表,2015,52(19):. LIU Yi-jun,Yang Hong-geng,Wang Jia-xing,Wang Ze.Voltage Sag State Estimation in Power System Based on Quantum-Behaved Particle Swarm Optimization[J].Electrical Measurement & Instrumentation,2015,52(19):.
基于量子行为粒子群优化算法的电压暂降状态估计
Voltage Sag State Estimation in Power System Based on Quantum-Behaved Particle Swarm Optimization
a new method of voltage sag state estimation based on the combination of the traditional fault position method and state estimation method in this paper. To combine the fault position method which only use the historical data and state estimation method which only use the limited monitoring data, voltage state estimation equation is obtained.And use quantum-behaved particle swarm optimization to solve the optimization problem in this paper. Compared with traditional optimization algorithm such as GA, the proposed algorithm can guarantee the global convergence, and has less control parameters and stronger randomness , so it’s more efficient to find the optimal solution. The proposed estimation method has been validated by using the IEEE 24 test system and the results obtained have been very satisfactory. The proposed method is applicable to voltage sag state estimation in any size grids which occurs symmetric and asymmetric faults.
