Thermal power generating unit system model parameter identification method

Abstract

The invention relates to a thermal power generating unit system model parameter identification method. A self-adaptive thought, a Cauchy variation factor and a gradient guidance crossover theory are introduced to improve a basic particle swarm algorithm, parameters of an established model are identified, a difference value between actual operation data and model data is regarded as an algorithm fitness function value, model parameters are identified and optimized to obtain a model suitable for a system, a main throttle valve opening degree-power model, a combustion system and a load system, which are obtained through identification by adopting the improved self-adaptive Cauchy variation gradient guidance crossover particle swarm algorithm, have good effect of outputting fitting field actual data, a transfer function obtained through identification structurally conforms to theoretical analysis and conforms to actual operation conditions of the unit in the aspect of parameters, and the thermal power generating unit system model parameter identification method has good unit analysis and research value.

Description

technical field [0001] The invention relates to a system modeling technology, in particular to a method for identifying parameters of a thermal power unit system model. Background technique [0002] In recent years, there have been many methods that use intelligent algorithms to replace traditional mechanism modeling methods to calculate control system model parameters. There are mainly the use of neural networks and fuzzy concepts to establish mathematical models of nonlinear systems; there are also methods to obtain mathematical models with higher precision through intelligent algorithms such as genetic algorithm and ant colony algorithm. These methods avoid the tedious process of traditional mechanism modeling, so they are widely used in thermal system modeling of power plants, and play a very important role in analyzing the operating characteristics and conditions of power plants. [0003] Particle Swarm Optimization (PSO) is a swarm-based intelligent evolutionary compu...

AI Technical Summary

Problems solved by technology

The simple particle swarm optimization algorithm has problems such as premature convergence (especially in dealing with complex multi-peak search problems), poor local optimization ability, and a certain possibility of falling into local optimal solutions, which will seriously affect the final results of the algorithm. The calculation result may cause the calculation result to deviate significantly from the actual value in severe cases

Images