Distributed optimal control of parallel water pumps in heat exchange station
Aiming at the problem that the optimization algorithm of parallel circulating pump in existing heat exchange station has insufficient control adaptability under centralized structure,an improved distributed parallel circulating pump optimization algorithm was proposed.Firstly,a distributed control system of parallel circulating pumps was established,and the mathematical model of the optimization problem was described,and the adaptive nonlinear factor was introduced into the objective function.Then,an improved distributed fruit fly optimization algorithm was designed,in which the controller of each water pump can complete the optimization of parallel circulating pumps only by interacting with adjacent controllers.In the olfactory search stage,the sinusoidal cosine strategy was used to replace the random strategy given individual distance and direction.Finally,the algorithm was simulated and verified by different parallel circulating pump systems in two actual heat exchange stations,and the performance was analyzed based on the simulation results.The results show that compared with the traditional algorithm,the improved distributed fruit fly optimization algorithm can obtain a better control strategy,with the characteristics of fast convergence,good stability and strong robustness.The algorithm can be applied to the parallel pump optimization problems of different systems,and has scalability.Compared with the centralized algorithm in the actual engineering verification,the total power and computing time of the proposed algorithm are reduced by 5.47％and 29.90％,respectively.Therefore,it can meet the demand for optimal distribution of the heat load of parallel water pumps in actual heat exchange stations.
heat exchange stationparallel water pumpdistributed control systemfruit fly optimization algorithmload optimization distribution
万方数据
维普
