Research On Parameter Identification Method Of Static Var Compensator Model Based On Improved Chicken Swarm Optimization Algorithm

With the continuous expansion of the scale of modern power system,the safe and stable operation of power grid is facing many major challenges.Static var compensator(SVC),as an important part of power system,is widely used in improving voltage quality and stability of power system.Its accurate model and parameters play an important role in power system operation analysis and simulation calculation.Therefore,it is of great theoretical and practical significance to study the effective parameter identification methods of SVC model and improve the accuracy of parameters of SVC model.In this paper,static var compensator is taken as the research object.On the basis of establishing the mathematical model of the static var compensator considering the characteristics of each link,a parameter identification method of the static var compensator model based on parameter identification theory,swarm intelligence optimization theory and local sensitivity analysis theory is proposed.The effectiveness and superiority of the method proposed in this paper are verified by simulation examples.The main research work and achievements are as follows:Aiming at the problem that Chicken Swarm Optimization(CSO)may have local optimum in high-dimensional and non-linear system identification,an Improved Chicken Swarm Optimization(ICSO)based on backward learning strategy and inertia weight reduction strategy is proposed in this paper.Specifically,on the basis of the original chicken swarm algorithm,the reverse learning strategy is used to generate the reverse solution of the initial population,select the better solution in the initial solution and the reverse solution,so as to improve the probability of finding the optimal solution.On this basis,the inertia weight reduction strategy is introduced to improve the population position update formula,so as to improve the search ability of the algorithm.The simulation results of test function show that ICSO algorithm is superior to the original algorithm in search ability and convergence speed.The feasibility and superiority of ICSO algorithm in SVC model parameter identification are verified by two-machine system.Aiming at the problem that the error of some parameters identification results may be large in the global identification of multi-parameters,a step-by-step identification method of model parameters of static var compensator based on local sensitivity analysis of parameters is proposed.By comparing the sensitivity mean of each parameter,the important identification parameters and secondary identification parameters are screened out,the important parameters are identified first,and the secondary parameters are set as typical values or merged into otheridentification processes,so as to reduce the dimension of the parameters to be identified and improve the identification accuracy.Taking two-machine and two-area four-machine system as an example,the proposed method is verified.The results show that the proposed method is feasible and superior in parameter identification of static var compensator model.The validity of the proposed method in actual power grid simulation is verified by the simulation example of parameter identification of SVC model in BPA.Aiming at the heavy task of static var compensator model parameter identification and the lack of visual simulation tools,this paper uses MATLAB GUI to build a visual simulation interface for SVC model parameter identification.According to the required functions,the graphical interface is created and the corresponding callback function is written.Simply reading the input and output data and calling the SVC model,the identification results can be obtained,which can effectively improve the identification efficiency and reduce the burden of staff.