| 引用本文: | 于军琪,高之坤,赵安军,周敏,虎群.改进并行粒子群算法用于冷却水系统节能优化[J].控制理论与应用,2022,39(3):421~431.[点击复制] |
| YU Jun-qi,GAO Zhi-kun,ZHAO An-jun,ZHOU Min,HU Qun.Improved parallel particle swarm algorithm for energy-saving optimization of cooling water system[J].Control Theory and Technology,2022,39(3):421~431.[点击复制] |
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| 改进并行粒子群算法用于冷却水系统节能优化 |
| Improved parallel particle swarm algorithm for energy-saving optimization of cooling water system |
| 摘要点击 1689 全文点击 640 投稿时间：2021-07-25 修订日期：2022-03-29 |
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| DOI编号 10.7641/CTA.2021.10667 |
| 2022,39(3):421-431 |
| 中文关键词 冷却水系统 运行参数 改进并行粒子群算法 节能优化 性能分析 |
| 英文关键词 cooling water system operation parameters improved parallel particle swarm optimization algorithm energy saving optimization performance analysis |
| 基金项目 西安咸阳机场三期扩建工程绿色能源站系统智能管控咨询与顾问项目技术服务项目(20210103), 国家重点研发计划项目(2017YFC0704100)资助. |
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| 中文摘要 |
| 针对冷却水系统优化问题提出一种改进并行粒子群(IPPSO)算法, 以系统能耗最小为优化目标, 以系统中
各设备的运行参数为优化变量进行求解. 在该算法中, 采用随机和混沌序列机制分别对两个种群的粒子进行初始
化, 使两种群在产生初期便具有不同特征; 并根据两种群特点, 采用不同惯性权重改进策略, 提高算法搜索能力; 同
时利用一种新迁移算子对种群间个体进行交换, 增强粒子多样性, 使种群向更高层次进化; 此外, 考虑到系统设备运
行数量为整数且受到系统设计总台数的限制, 引入穷举法机制对系统中设备部分运行参数进行求解, 减少最优解验
证工作量, 缩短优化时间. 最后对某实际冷却水系统进行了详细测试, 结果表明, 使用IPPSO算法对设备运行参数优
化后, 冷却水系统总能耗降低12.49%, 具有较好的节能效果. 同时相比于其他算法, IPPSO能得到更好的优化策略,
且在收敛性、计算复杂度和鲁棒性方面具有优势. |
| 英文摘要 |
| Aiming at the optimization problem of cooling water system, an improved parallel particle swarm optimization
(IPPSO) algorithm is proposed. The optimization goal is to minimize the energy consumption of the system, and the
equipment operating parameters in the system are taken as optimization variables to solve the problem. In this algorithm,
the random and chaotic sequence mechanisms are used to initialize the particles of the two groups respectively, so that the
two groups present different characteristics at the initial stage of generation; based on the characteristics of the two groups,
different inertia weight improved strategies are adopted to improve algorithm search ability; at the same time, a new migration
operator is proposed to exchange individuals between the groups to enhance the diversity of particles. In addition,
because the number of system equipment running is an integer and limited by its total equipment number, an exhaustive
method is introduced. Some equipment operating parameters in the system are optimized, reducing the workload of optimal
solution verification and shortening the optimization time. Finally, a detailed test is carried out on an actual cooling water
system, and the results show that after using the IPPSO algorithm to optimize the equipment operating parameters, the
total energy consumption of the cooling water system is reduced by 12.49%, which performs a good energy-saving effect.
Simultaneously, compared with the other algorithms, IPPSO can obtain better optimization strategies, and has advantages
in convergence, computational complexity and robustness. |
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