Generator excitation system parameter identification algorithm based on improved grey wolf algorithm

Abstract

The invention discloses a generator excitation system parameter identification algorithm based on an improved grey wolf algorithm. The generator excitation system parameter identification algorithm comprises: establishing an original model and an actual system model of an excitation system in a no-load state; identifying the actual system model entering the linear region through an improved grey wolf algorithm to obtain linear part parameters; inputting the linear part parameters into an actual system model; and identifying the actual system model which enters the non-linear region and is substituted with the linear part parameters through an improved grey wolf algorithm to obtain the non-linear part parameters. On the basis of the grey wolf algorithm, a convergence factor nonlinear decreasing strategy and a grey wolf grouping alternate chasing strategy are provided, population diversity of wolf groups is enhanced, and the defect that the algorithm is prone to falling into local optimum is overcome. The grey wolf algorithm is applied to identification of excitation system parameters, identification of the excitation system parameters is effectively achieved by improving the grey wolf algorithm, and an identification result proves that the identification precision and stability of the improved grey wolf algorithm are superior to those of a traditional grey wolf algorithm.

Description

technical field [0001] The invention belongs to the field of electric power systems, and in particular relates to an identification algorithm of generator excitation system parameters based on an improved gray wolf algorithm. Background technique [0002] System identification is to select a model from a group of models according to certain criteria, so that it can best fit the dynamic or static characteristics of the actual system reflected by the input and output observation data of the system. The selection of the identification method plays a very important role in the identification accuracy. According to the identification theory, the identification methods can be divided into two types: classical identification method and modern identification method. [0003] The development of classic system identification methods has been relatively mature and perfect, including step response method, impulse response method, frequency response method, correlation analysis method, s...

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Problems solved by technology

[0010] Aiming at the deficiencies of the prior art, the purpose of the present invention is to provide a generator excitation system parameter identification algorithm based on the improved gray wolf algorithm, so as to solve the problem in the prior art that the parameter identification of the nonlinear generator excitation system cannot be performed well. The problem

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