Identification of Load Object Model of Gas-steam Combined Cycle Unit based on TSL-IPSO
Aiming at the problem that the traditional identification methods and particle swarm optimiza-tion(PSO)algorithms had low optimization accuracy and slow convergence speed in identifying the load object model of gas-steam combined cycle units,a dimensional learning strategy-based two-swarm learn-ing improved particle swarm optimization(TSL-IPSO)algorithm was proposed to optimize the global search ability and local improvement ability of PSO algorithm.The load object models identified by TSL-IPSO algorithm and PSO algorithm were compared and validated using the load point data of 257.4 MW and 436.07 MW gas-steam combined cycle units obtained through open-loop step experiments.The re-sults show that compared with PSO,differential evolution(DE)and genetic algorithm(GA),the TSL-IPSO algorithm has the smallest root mean square error and average absolute percentage error of the iden-tification model,the best convergence effect of the fitness change curve,and better model identification accuracy and optimization performance.
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