Parameter identification of steam turbine governor system based on DEPSO algorithm

Abstract

It is a research hotspot that the evolutionary algorithm is applied to the nonlinear parameters identification of turbo-generator speed governor system, but the single evolutionary algorithms have varying degrees of defects: the premature convergence and slow convergence speed. A hybrid optimization algorithm (DEPSO) is proposed to overcome single evolutionary limitations based on the combination of differential evolution (DE) and particle swarm optimization (PSO). Differential mutation, crossover and selection operators are employed to produce a personal best position in the process of particle evolution, the direction of particle evolution is optimized. Combined with the project of dynamic measurement of generator set located in Guizhou Power System, the parameters of the speed governor system are identified by the method of DEPSO based on the typical dynamic model of turbo-generator unit and the data got from field test. The results indicate that the method of DEPSO can validly identify parameters of speed governing system and has the advantages of high accuracy and effectiveness for operation.