基于改进粒子群算法的船用汽轮机DEH智能控制优化  

Research on DEA intelligent control optimization of marine steam turbine based on improved particle swarm optimization

作　　者：林安 张磊[1] 陈国兵[1] 李昆锋[1] LIN An;ZHANG Lei;CHEN Guo-bing;LI Kun-feng(School of Power Engineering,Naval University of Engineering,Wuhan 430032,China)

机构地区：[1]海军工程大学动力工程学院,湖北武汉430032

出　　处：《舰船科学技术》2022年第13期126-131,共6页Ship Science and Technology

基　　金：国家自然科学基金资助项目(51702364)。

摘　　要：针对船用汽轮发电机组大幅变负荷与复杂外界影响因素等特点,剖析船用汽轮发电机组DEH系统结构和运行特性,构建汽轮机及其DEH系统模块化数学模型;针对传统粒子群算法易陷入局部最优导致收敛精度不足问题,提出引入自适应递减权重法和类似遗传算法的选择、杂交、变异操作进行改进,并采用标准测试函数验证了改进粒子群算法的优化精度与计算效率。基于改进粒子群算法实现DEH系统的参数辨识,其辨识精度和效率均优于典型智能算法。根据参数辨识结果,构建DEH系统的传递函数模型,并利用改进粒子群算法实现了汽轮机组不同工况及影响因素条件下的PID参数自整定,为船用汽轮发电机组DEH系统控制及运行优化提供支撑。In view of the characteristics of large-scale load change and complex external factors of marine steam turbine generator,the paper analyzes the structure and operation characteristics of DEH system of marine steam turbine generator set,constructs the modular mathematical model of steam turbine and DEH system.Aiming at the problem that traditional particle swarm optimization algorithm is prone to local optimization and leads to insufficient convergence accuracy,the paper proposes the selection of adaptive decreasing weight method and similar genetic algorithm The hybrid and mutation operations are improved,and the optimization accuracy and calculation efficiency of the improved PSO are verified by standard test function;the parameter identification of DEH system is realized based on the improved PSO,and its identification accuracy and efficiency are better than the typical intelligent algorithm;according to the parameter identification results,the transfer function model of DEH system is constructed,and the improved PSO is used to realize the performance of the DEH system.The PID parameters of the turbine set are self-tuning under different working conditions and influencing factors,which provides a strong support for the DEH system control and operation optimization of marine steam turbine generator set.

关 键 词：电液调节系统 粒子群算法 参数辨识 PID参数 

分 类 号：TK263.7[动力工程及工程热物理—动力机械及工程]