一个基于MPPT的高速马群优化算法,用于PV电池充电系统的动态线性主动干扰排斥控制
Al-Wesabi Ibrahim1, Jiazhu Xu2, Imad Aboudrar3
1College of Electrical and Information Engineering, Hunan University, Hunan, 410083, China. Ibrahim@hnu.edu.cn.
Scientific reports
|January 25, 2025
概括
本研究介绍了一种新的马群优化算法 (HHOA),与线性活性干扰拒绝控制 (LADRC) 结合,用于在不同条件下增强光伏 (PV) 系统的电力提取. 该HHOA-LADRC方法实现了优越的全球最大峰值跟踪,提高了效率和更快的响应时间.
科学领域:
- 可再生能源系统可再生能源系统
- 控制理论 控制理论
- 优化算法 优化算法
背景情况:
- 光伏 (PV) 系统在动态和静态环境条件下 (DSEC) 需要高效的最大功率点跟踪 (MPPT).
- 传统的方法,如 Perturb & Observe (P&O) 和现有的元启发算法,与波动的条件和趋同速度作斗争.
- 需要强大的控制策略来处理光伏MPPT中的系统不确定性和干扰的需求至关重要.
研究的目的:
- 提出和评估一种创新的HHOA-LADRC方法,以优化光伏系统的最大功率提取.
- 提高MPPT技术的抗干扰能力和响应率.
- 为了证明所提出的方法在传统和其他元启发算法上的优越性.
主要方法:
- 应用马群优化算法 (HHOA),一种生物灵感的技术,用于优化电力提取.
- 整合线性主动干扰拒绝控制 (LADRC) 来调节HHOA的参考输出电压.
- 在DSEC下对P&O,CPSO,GHO和DPSO算法进行比较模拟分析.
- 使用NI PXIE-1071硬件在循环 (HIL) 原型进行实验验证.
主要成果:
- 在HHOA-LADRC方法成功地跟踪全球最大峰值 (GMP) 减少功率波动和更快的收.
- 模拟结果显示,与传统和其他元启发式MPPT技术相比,显著改善.
- 实验调查证实了HHOA-LADRC的有效性,实现了99%以上的效率,并实现了更快的融合和最小的振荡.
结论:
- 拟议的HHOA-LADRC方法为光伏最大功率点跟踪提供了一个高度有效和高效的解决方案.
- 这种混合控制策略在动态和静态环境条件下显著提高了系统性能.
- 该方法表现出强大的抗干扰能力和快速响应,使其适用于现实世界的光伏应用.
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