基于改进的混合优化算法对光伏发电的最大功率点跟踪的研究
1College of Electrical and Computer Technology, Jilin Jianzhu University, Changchun, Jilin, China.
Scientific reports
|February 1, 2025
概括
本研究介绍了一种改进的算法,将粒子群优化 (PSO) 和引力搜索算法 (GSA) 结合起来,在不同条件下在光伏阵列中实现高效的最大功率点跟踪.
科学领域:
- 可再生能源系统可再生能源系统
- 优化算法 优化算法
- 电气工程 电气工程
背景情况:
- 由于环境因素,光伏 (PV) 阵列表现出复杂的功率-电压 (P-V) 曲线,具有多个峰值.
- 传统的重力搜索算法 (GSA) 在最大功率点跟踪 (MPPT) 中与局部最佳和次优性能作斗争.
研究的目的:
- 为了提高光伏阵列在动态和静态阴影条件下的MPPT性能.
- 为太阳能系统开发更强大,更准确的优化算法.
主要方法:
- 一个改进的算法集成粒子优化 (PSO) 来优化GSA参数.
- 纳入动态引力常数用于PSO参数调整和Levy飞行,以增强全球搜索.
- 用同步链模型和模拟来验证算法的有效性.
主要成果:
- 与传统方法相比,增强的算法在识别最大功率点方面表现出卓越的速度和稳定性.
- 在静态和动态遮阳场景下实现了最大功率点的有效跟踪.
- 观察到振荡减轻,并提高了功率输出预测的准确性.
结论:
- 拟议的混合PSO-GSA算法为光伏系统的MPPT提供了显著的进步.
- 这种改进的方法为最大限度地从太阳能阵列中获取能源提供了更可靠的解决方案,特别是在具有挑战性的环境条件下.
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