A new parameters identification of single area power system based LFC using Segmentation Particle Swarm Optimization (SePSO) algorithm

The accuracy of the power system model is important in investigating the transient phenomena of load frequency control (LFC). In this paper, Segmentation Particle Swarm Optimization (SePSO) method is proposed for governor-turbine model identification of single area power plant. The method is acquired based on a combination of segmentation and Particle Swarm Optimization (PSO) algorithms, in which the segmentation is used to recognize the local and global optimal point problem of PSO. The tests of the investigated governor system performed to obtain the step response to identify all parameters of the governor-turbine model. Finally, to verify the effectiveness of the proposed identification, three disturbance cases and three control parameters were implemented by comparing with the common PSO and GA-PSO algorithms. The results show that the proposed method performed better in terms of accuracy and computation time.