In order to meet the parameter tuning requirements of main steam temperature controller under fast and deep variable load conditions of thermal power units

the selection operator

adaptive learning factor and adaptive inertia weight coefficient are introduced into the standard particle swarm optimization algorithm

and the multi strategy zonal exploration particle swarm optimization algorithm is proposed. According to the fitness evaluation index

the population is divided into four regions: standard particle swarm operation region

selection operator operation region

adaptive learning factor operation region and adaptive inertia weight coefficient operation region

so as to give full play to the advantages of each optimization strategy

and ensure that the algorithm has higher convergence accuracy and faster convergence speed. Compared with four other improved particle swarm optimization algorithms

the results show that the proposed algorithm has high optimization accuracy and short convergence time. Moreover

the proposed algorithm

attenuation curve method and three improved particle swarm optimization algorithms are applied to the parameter optimization of main steam temperature cascade PID controller respectively. The simulation results verify the effectiveness of the proposed algorithm.