船用汽轮机变工况的遗传粒子群优化智能控制方法研究
Genetic Particle Swarm Optimization Control Method for Marine Steam Turbine with Variable Working Conditions
张磊 1李源 2林安 1袁陈臣1
作者信息
- 1. 海军工程大学动力工程学院, 武汉 430033
- 2. 武警海警总队第五支队, 三亚 572032
- 折叠
摘要
为提升船用汽轮发电机组大幅变工况时的控制精度和鲁棒性,以船用汽轮机调节系统各部件的模块化数学模型为基础,建立汽轮机组数字电液(digital electric hydraulic,DEH)闭环模糊比例、积分、微分(proportion integration derivative,PID)控制模型;融合遗传算法的选择、交叉、变异和自适应递减权重法,提出遗传粒子群智能优化算法,并结合标准测试函数验证提出算法具有较高的收敛速度和精度;基于遗传粒子群智能优化算法建立汽轮机变工况自适应智能模糊PID控制模型,实现模糊PID的量化因子与比例因子最优化设计,进而开展船用汽轮发电机组大幅变工况动态特性及扰动因素影响分析,结果表明本文建立的自适应智能模糊PID控制模型具有更好的控制稳态性能与鲁棒性,为船用汽轮机组大幅度变工况智能控制优化设计提供了有力的技术支撑.
Abstract
In order to improve the control accuracy and robustness of marine turbine generator sets under large changing working conditions, a closed-loop fuzzy PID( proportion integration derivative) control model of steam turbine units was established based on the modular mathematical model of each component of marine turbine regulation system. Combining the selection, crossover, mutation and adaptive decreasing weight method of genetic algorithm, a genetic particle swarm intelligent optimization algorithm was proposed, and combined with the standard test function to verify that the proposed algorithm has high convergence speed and accuracy. Then, based on the genetic particle swarm intelligent optimization algorithm, an adaptive intelligent fuzzy PID control model of the steam turbine un-der variable working conditions was established to realize the optimal design of the quantization factor and proportional factor of the fuzzy PID,and the dynamic characteristics and the dynamic characteristics of the marine steam turbine-generator set under greatly variable working conditions were carried out. The results show that the adaptive intelligent fuzzy PID control model established has better control steady-state performance and robustness. The research provides a strong technical support for the intelligent control and optimization de-sign of marine steam turbine units with greatly variable working conditions.
关键词
汽轮机/变工况/比例、积分、微分(proportion/integration/derivative,PID)/粒子群/控制Key words
steam turbine/variable condition/PID(proportion integration derivative)/PSO(particle swarm optimization)/control引用本文复制引用
出版年
2023
NETL
NSTL
维普
万方数据