Research On Multi-Objective Particle Swarm Optimization For Unit Coordinated Control System

The coordinated control system is an important unit-level control system in the thermal power unit automation system.The control quality of the coordinated control system is good or bad,which directly affects the safety and economic operation of the unit.With the rapid development of computer technology,artificial intelligence and other technologies,the application of intelligent optimization algorithm to industrial control system optimization has become an important research direction,and it also represents an important development trend of unit unit automation.Due to the strong nonlinearity of the controlled object of the coordinated control system,the serious coupling between the furnace and the large inertia of the furnace side,the parameter setting of the machine and furnace controller becomes more difficult.Therefore,the intelligent optimization algorithm has certain engineering application research value in the parameter optimization of unit unit coordinated control system.This paper first studies the object model of the unit unit coordinated control system.According to the literature,the 660 MW unit unit coordinated control system is simplified into a double-input and double-out model,and the dynamic characteristics of the model are analyzed.Secondly,the theoretical research on standard particle swarm optimization and multi-objective particle swarm optimization algorithm is carried out.For the single-objective particle swarm optimization algorithm,the inertia weight is designed in a linearly decreasing manner.For the particles of external non-inferior archives in the multi-objective particle swarm optimization algorithm,the evolutionary strategies of adaptive grid,mutation and roulette operator selection in the algorithm are analyzed to maintain the diversity and convergence of the solution.Reduce the probability that the algorithm falls into local optimum,enhance the ability of the algorithm to optimize the aggregation to the actual Pareto front,and use MATLAB to program.The effectiveness of the algorithm is verified by multi-objective test set function simulation.Finally,the decoupling method of coordinated control system is studied,and the parameters optimization strategy of coordinated control system machine and furnace PID controller is given.On the post-series compensation decoupling scheme,the simplified control scheme of a one-way decoupling network is reasonably simplified for the actual unit characteristics.On the turbine side,the integral of the ITAE error moment is used as the objective function,and the single-objective particle group is used to optimize the PID controller on the turbine side;on the boiler side,the multi-objective particle swarm optimization algorithm is used to optimize,according to the "stable,accurate,fast" It is required to establish a multi-objective optimization fitness function on the boiler side,with the aim of solving the problem of slow response and serious overshoot on the boiler side.Combined with the improved multi-objective particle swarm optimization algorithm,using MATLAB programming and Simulink simulation,the non-inferior solution set of the boiler-side PID controller is obtained,and then a solution is selected from the non-inferior solution set as the optimal parameters of the controller.The simulation results show that the PID parameters of the coordinated control system are better than the traditional experience trial and error setting method,good control effect is obtained.