Research On PSS Parameter Optimization Based On Improved Prony Identification And Beetle Swarm Algorithm

With the interconnection of the national power grids,the scale of the power system is becoming larger and larger.The large-scale investment of long-distance transmission lines and fast excitation devices in the inter-regional power grid has led to a decrease in the total damping of the system,so low-frequency oscillations is prone to occur,which causes great security risks to the power system.By installing power system stabilizer(PSS)in the excitation system,the system damping can be effectively improved and the low-frequency oscillation can be suppressed.Aiming at the problem of power system low frequency oscillation identification and stabilizer design,this thesis proposes an Improved Prony method to identify low frequency oscillation and the beetle swarm algorithm to optimize PSS parameters.Firstly,the mechanism,analysis,identification and suppression of low frequency oscillation are studied.Based on the research of negative damping mechanism,Prony analysis method is used to identify the system oscillation mode.Aiming at the problem that the Prony analysis method is sensitive to noise,the wavelet threshold denoising method is used to reduce the noise,and the feasibility of the improved Prony method was verified by a test example.Combined with the mechanism of negative damping,the principle of PSS’s suppression of low-frequency oscillation was analyzed,and PSS,which is commonly used in practical engineering is used as the research object to optimize the parameters.Secondly,PSS improves the damping of system oscillation mode by adding damping torque.Improper selection of unit parameters will enhance the damping of some oscillation modes,but it may also reduce the damping of other oscillation modes,and the total damping of the system will be affected.Therefore,it is necessary to reasonably adjust PSS parameters and consider the coordination and optimization of various parameters.This thesis proposes a PSS parameter tuning method using an improved beetle search algorithm.Aiming at the shortcomings of the beetle search algorithm(BAS)that the global search ability is not strong,the BAS algorithm and the particle swarm optimization(PSO)are combined to obtain the beetle swarm algorithm(BSO),used three typical test functions to verify the performance of the algorithm,and compared the BSO algorithm with the PSO algorithm and the layered fertility group of chaotic particle swarm optimization algorithm(HUCPSO),the results show that BSO algorithm is better for highdimensional functions,can achieve global search,has fewer iterations and takes less time to converge.Finally,build a single-machine infinity system model and a four machine two area system model in Matlab/Simulink.The objective function is the secondary performance index of the eigenvalue damping ratio of the system,and the BSO algorithm was used as an optimization tool,and the actual problem was transformed into a parameter optimization problem of PSS.The sampled data was identified by the improved Prony method after denoising,and compared with the PSO algorithm and the HUCPSO algorithm under the same conditions.The large and small interference analysis was carried out in the single-machine infinity system,and the performance of the algorithm was initially verified;the large and small interference analysis was carried out in the four-machine two-area system under different operation modes,which further verified that the algorithm has better damping characteristics and robustness,and can suppress low frequency oscillation more quickly.There are 43 figures,11 tables and 66 references in this thesis.