在部分遮阳条件下用于光伏系统的最大功率点追踪的改进的鹿群优化算法
Gang Zheng1, Wenchang Wei1, Heming Jia2
1College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China.
Biomimetics (Basel, Switzerland)
|August 27, 2025
概括
传统的最大功率点跟踪 (MPPT) 难以实现部分遮. 一个改进的鹿群优化 (IEHO) 算法快速找到全球最大功率点,在不同条件下提高光伏系统的效率.
科学领域:
- 可再生能源系统
- 光伏电力转换
- 优化算法
背景情况:
- 在光伏系统中,部分遮阳条件 (PSC) 会导致功率电压特性出现多个峰值.
- 传统的最大功率点跟踪 (MPPT) 算法通常会陷入局部最佳状态,从而降低能量转换效率.
- 在复杂的环境条件下快速有效地定位全球最大功率点对于光伏性能至关重要.
研究的目的:
- 在各种天气条件下提出改进的群优化 (IEHO) 算法,用于在光伏系统中快速全球最大功率点跟踪 (MPPT).
- 提高MPPT性能和在部分遮阳下运行的光伏系统的能量转换效率.
主要方法:
- 开发了一种改进的鹿群优化 (IEHO) 算法,采用以捕食风险概率为指导的位置更新机制.
- 引入了三角行走策略以提高算法的逃离局部最佳的能力.
- 实现了内存引导的重定向策略,通过跳过冗余的历史工作周期计算来优化融合速度.
主要成果:
- 与其他元启发式算法相比,IEHO算法在各种天气条件下表现出卓越的性能.
- 在测试条件下达到99.99%的平均追踪效率.
- 实现了0.3886秒的平均追踪时间, 显示了接近速度的显著改善.
结论:
- 拟议的IEHO算法有效地解决了MPPT在部分遮下的局部最佳的挑战.
- IEHO显著提高了光伏系统全球最大功率点跟踪的速度和准确性.
- 该算法为在动态环境中提高光伏系统的整体能量转换效率提供了一个有希望的解决方案.
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