摘要
针对火电厂母管制锅炉的母管压力协调优化控制问题,根据负荷需求情况调整锅炉负荷的作用,而母管压力的协调控制难点在于如何进行合理的负荷分配。为了更好的解决母管压力的协调控制问题,提出一种新的锅炉状态评价方案,并且把新的锅炉评价方案与Hopfield神经网络进行融合,采用自适应神经网络对负荷分配进行优化计算,得到最优的分配结果,最终分配结果表明新方法能够实现对母管制机组的负荷进行良好的分配,同时减少能源的消耗。仿真数据表明,上述锅炉控制方案合理,为负荷分配优化提供了可行方法。
The coordinated control of the mother tube boiler in the thermal power plant plays an important role in adjusting the boiler load according to the load demand, however the difficulty of the coordinated control of the main tube pressure lies in how to carry out the reasonable load distribution. In order to better solve the problem of main tube pressure control, a new boiler state evaluation scheme was proposed in the paper. The new boiler evaluation scheme and Hopfield neural network were integrated, and the optimal allocation results were obtained using the adaptive neural network to optimize the load distribution. The final results show that this method can achieve a good distribution of the main tube control unit, and reduce the consumption of energy at the same time. The simulation data illustrate that the evaluation scheme of the boiler is reasonable, and the method of load distribution is feasible.
出处
《计算机仿真》
CSCD
北大核心
2016年第7期180-183,216,共5页
Computer Simulation
关键词
母管制锅炉
母管压力
状态评价
负荷分配
神经网络
Mother tube boiler
Mother tube pressure
Status evaluation
Load distribution
Neural network
