使用修改的量子启发粒子群方法优化光伏系统的参数
Zia Ur Rehman1, Obaid Ur Rehman1, Amr Munshi2
1Department of Electrical Engineering, Sarhad University of Science and IT, Peshawar, Pakistan.
Scientific reports
|February 9, 2026
概括
准确估计光伏 (PV) 系统参数对于效率至关重要. 一种新的修改量子灵感的粒子群集优化 (MQPSO) 方法显著提高了参数估计的准确性比标准方法.
科学领域:
- 可再生能源工程可再生能源工程
- 计算智能是一种计算智能.
- 电气工程 电气工程
背景情况:
- 越来越多的光伏 (PV) 能源需求需要可靠的光伏系统性能.
- 准确估计内部光伏系统参数对于提高系统可靠性和效率至关重要.
- 传统的优化方法经常与光伏系统的非线性性质相斗争,导致过早的融合.
研究的目的:
- 解决因非线性和传统方法的局限性而导致的估计光伏系统参数的挑战.
- 引入和评估一个改进的量子行为粒子集群优化 (QPSO) 算法用于PV参数估计.
- 探索拟议的修改量子灵感粒子群集优化 (MQPSO) 方法的有效性.
主要方法:
- 修改量子启发的粒子群集优化 (MQPSO) 算法的开发.
- 将双重吸引力,精英主义战略和地方精细化纳入QPSO框架.
- 在三种不同的光伏模型 (SDM,DDM,TDM) 中对MQPSO与标准QPSO进行定量分析和比较.
主要成果:
- 与传统的QPSO相比,MQPSO在所有测试模型中估计光伏参数方面表现优越.
- 实现了显著的根平均平方误差 (RMSE) 减少:SDM为24.74%,DDM为59.32%,TDM为14.99%.
- MQPSO的增强收行为和性能得到了定量验证.
结论:
- 拟议的MQPSO方法为光伏参数估计提供了更有效和高效的方法.
- MQPSO克服了传统优化技术的局限性,提供更高的准确性和可靠性.
- 该研究强调了MQPSO在推进光伏系统建模和性能优化方面的潜力.
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