摘要
为了解决电力系统的节能优化和有效控制的问题,论文对分散控制系统进行了充分研究,将分散控制系统应用于电力系统的设计之中;同时对传统的粒子群智能搜索算法分析进行了分析,研究了其特点和不足之处,在传统的PSO节能控制方法的基础上,提出了一种多重自适应的粒子群优化算法,该算法相较于传统粒子群搜索算法具有较高的搜索精度;论文结合分散控制系统与所提算法设计与实现了一种新的基于DCS的电力节能优化控制系统;数值仿真的结果说明了使用所提出的粒子群算法的基于DCS的电力节能优化控制系统在电力调度最佳节点的搜索精确度要高于相同条件下的一般的电力控制系统,并且能有效地对电力能耗进行优化,达到节能目的,且可减少一部分人力资源,具有较高的实用性。
In order to solve the problem of energy--saving optimization and effective control of power system, the decentralized control system is fully studied in this paper, and the decentralized control system is applied to the design of power system; At the same time, the traditional particle swarm intelligence search algorithm is analyzed, and its characteristics and shortcomings are studied. This paper proposes a multi--adaptive particle swarm optimization algorithm based on the traditional PSO energy--saving control method. Compared with the traditional particle swarm search algorithm, the proposed algorithm has higher search accuracy. Combining the advantages of decentralized control system and the proposed algorithm, a new energy--saving optimization control systemis designed and realized in this paper. The simulation results show that the new particle swarm algorithm has higher search precision than other particle swarm algorithm under the same condi- tions. The energy--saving optimization control system using this algorithm can effectively optimize the power consumption, and this system can also reduce part of human resources and has higher practicability.
出处
《计算机测量与控制》
2018年第2期113-116,共4页
Computer Measurement &Control
关键词
节能控制
DCS
粒子群算法
自适应
energy-- saving control
particle swarm optimization
adaptive
DCS
