Research On Coordination And Optimization Of Multi-Machine System PSS Parameters Based On Improved Artificial Bee Colony Algorithm

In recent years, china's electric power system has developed rapidly. With the application of nation-wide power grid interconnection and China's power transmission from the West to the East, the scale of power grid is more and more large. Due to the long-distance and weak connection of large regional power grid and the use of a variety of fast excitation systems, low-frequency oscillation has happened in power grid systems sometimes, which has serious impact on the stability of power systems. Power system stabilizer(PSS) of excitation system can damp low frequency oscillation effectively, and improve security and economy of power system.On the analysis of optimization of PSS parameters of some commonly-used algorithms shortcomings, a new intelligent algorithm named as artificial bee colony(ABC) is used to solve the problem.In the study and analysis of ABC algorithm, to aim at some shortcomings such as slow convergence speed and low searching precision, an improved ABC algorithm is proposed and then applied to solve the optimization of PSS parameters problem in this dissertation. The main content is below in this thesis.(1) On the analysis of optimization of PSS parameters of commonly-used algorithms shortcomings, and a new intelligent optimization algorithm is proposed which is necessity and possibility for solving the PSS parameters optimization problems.(2) The Prony algorithm based on comparison and research on low-frequency oscillation analysis is determined, and applied in an example simulation. The results show that the Prony algorithm is efficiency in analyzing the low-frequency oscillation problem.(3) To aim at the traditional ABC shortcomings, an improved ABC algorithm that based on Nelder-mead simplex method(NMSM) is proposed. The algorithm migrates periodically the optimal bee from ABC to NMSM and passes the new exploration point from NMSM into ABC. ABC can improve its local exploiting capability by NMSM, and NMSM can get away from local optimum by ABC. The algorithm enhances the global searching and local exploiting ability. Furthermore, in order to improve convergence speed, an improved searching strategy is applied in onlooker bee colony. Six benchmark functions are used to evaluate the performance of the proposed method. Experimental results show that the improved algorithm enhances faster convergene speed and higher searching precision.(4) The Prony algorithm is used to identify the eigenvalue of low frequency oscillation modes, and calculate the damping ratio which can reflect the damping level of multi-machine system. The objective function based on the damping ratio is constructed and the improved ABC algorithm is applied to optimize PSS parameters. 4-machine 2-region system and 10-machine 39-node system are chosed as test examples. Two simulation examples results verify the necessity of PSS parameters optimization and the superiority of the improved ABC algorithm.