| 摘 要: 为实现水培营养液水质参数的高效、精确控制,减少设备供能产生的碳排量,构建了一个基于粒子群优化(Particle Swarm Optimization,PSO)算法和最大功率点跟踪(Maximum Power Point Tracking,MPPT)算法的水培智能控制系统。用PSO算法优化模糊控制器的量化、比例因子,加入Smith预估器补偿反馈时延,对pH为4.5、电导率(Electrical Conductivity,EC)为0 mS/cm的营养液进行精确调控。经过优化,分别在44 s和43 s后达到预设值,并能维持稳定状态。建立光伏发电模块,引入MPPT算法,缩短跟踪时长至0.04 s。结果表明,该系统能提高营养液水质参数的调节精度,缩短控制时长,增强水培环境的稳定性;同时,能提升发电效率,实现节能减排。 |
| 关键词: 粒子群优化算法;最大功率点跟踪;水培智能控制;模糊控制;Smith预估器;光伏发电 |
|
中图分类号: TP273
文献标识码: A
|
| 基金项目: 镇江市重点研发计划(SNY20210130007) |
|
| Design and Implementation of Solar Hydroponic Intelligent Control System Based on Particle Swarm Optimization Algorithms |
|
ZHANG Jing1, TU Xiaotong1, LIU Xiaomei2
|
(1.School of Electrical and Inf ormation Engineering, Jiangsu University, Zhenjiang 212013, China;
2.Jiangsu Kemao Inf ormation Technology Co., Ltd., Zhenjiang 212000, China)
jszj08062000@163.com; 1310812704@qq.com; 1151024312@qq.com
|
| Abstract: To achieve efficient and precise control of hydroponic nutrient solution quality parameters while reducing carbon emissions generated by equipment energy consumption, this paper proposes to develop a hydroponic intelligent control system based on Particle Swarm Optimization (PSO) algorithm and Maximum Power Point Tracking (MPPT) algorithm. The PSO algorithm is used to optimize the quantization and proportional factors of a fuzzy controller, incorporating a Smith predictor to compensate for feedback delay and accurately regulating a nutrient solution with a pH of 4.5 and Electrical Conductivity (EC) of 0 mS/cm. After optimization, the system reaches the preset values in 44 seconds and 43 seconds, respectively, and maintains a stable state. A photovoltaic power generation module is established, and the MPPT algorithm is introduced to reduce the tracking duration to 0.04 seconds. The results indicate that this system improves the adjustment accuracy of nutrient solution quality parameters, shortens the control time, and enhances the stability of the hydroponic environment; at the same time, it increases power generation efficiency and achieves energy conservation and emission reduction. |
| Keywords: PSO algorithm; MPPT; hydroponic intelligent control; fuzzy control; Smith predictor; photovoltaic power generation |
