Particle Swarm Based Swarm Intelligence Theory Research On Application Of System Optimization

Domestic thermal power development toward large capacity and high parameters.It has inproved the efficiency of coal-fired, reduced greenhouse gas emission, and put forward higher requirements for the quality of the thermal control system. Currently, simple and robustness PID controller is commonly used in power plants. How to select the optimal controller parameters become the key to improve controller performance. The rapid development of swarm intelligence algorithm provides a more intelligent optimization method for thermal control system. Particle swarm optimization algorithm is a swarm intelligence algorithm, which is proposed based on the imitating of natural foraging behavior of birds. It is easy to implement, quick calculation, and has been the focus of researchers in control system optimization.First of all, the paper introduced the basic principle and algorithm of PID controller, presented the classic and intelligent method for controller parameters optimization, and provided quality adjustment type and error integral objective functions. We can select the appropriate objective function for different object to optimize the controller parameters. The paper introduced the basic principles of particle swarm optimization, pointed out its shortcoming. And proposed chaotic quantum particle swarm optimization algorithm by combining chaos and quantum, and analyzed with test function. The results show that, CQPSO algorithm can quick jump out of local convergence, and improve the search speed and optimized accuracy. CQPSO algorithm successfully optimized the PID controller parameters of furnace pressure control system, it shows the applicability of the algorithm in controller parameters optimization aspect.Boiler main steam temperature is a self-balancing object, while boiler steam is non self-balancing. They are the most important and most difficult parameters to control. The fresh steam temperature and boiler steam level whether within the prescribed limits relate to the the safety and economy of the thermal power unit. This paper use the chaotic quantum particle swarm optimization algorithm, selecting the appropriate objective function based on object properties, separately optimized the PID controller parameters of main steam temperature control system and feedwater control system. The simulation results show that, chaos quantum particle swarm optimization control system has a small overshoot and fast response, anti-interference ability, able to meet the quality requirements of thermal complex objects, The promotion applies a certain value.