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.
2022,44(13): 126-131 收稿日期：2021-03-18
DOI：10.3404/j.issn.1672-7649.2022.13.028
分类号：TK263.7
基金项目：国家自然科学基金资助项目(51702364)
作者简介：林安(1996-),男,硕士研究生,研究方向为舰船动力及热力系统的科学管理
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