生物灵感算法的性能比较,用于优化基于ANN的MPPT预测,用于光伏系统
Rafael Rojas-Galván1, José R García-Martínez2, Edson E Cruz-Miguel2
1Facultad de Ingeniería, Universidad Autónoma de Querétaro, Santiago de Queretaro 76010, Mexico.
Biomimetics (Basel, Switzerland)
|October 25, 2024
概括
灰狼优化器 (GWO) 最好增强人工神经网络 (ANN) 最大功率点跟踪 (MPPT) 在部分阴影下的太阳能. GWO平衡了预测准确度和计算速度,提高了光伏系统的效率.
科学领域:
- 可再生能源系统可再生能源系统
- 在工程领域的人工智能.
- 优化算法 优化算法
背景情况:
- 光伏 (PV) 系统在部分遮阳条件下保持峰值能量输出面临挑战.
- 人工神经网络 (ANN) 用于最大功率点跟踪 (MPPT),但它们的性能可能在没有高级优化的情况下是次优的.
- 生物启发的算法为增强基于ANN的MPPT系统的准确性和效率提供了潜力.
研究的目的:
- 为了比较四种生物灵感优化算法的有效性:灰狼优化器 (GWO),粒子群优化 (PSO),松鼠搜索算法 (SSA) 和子搜索 (CS).
- 增强基于ANN的MPPT系统,以在光伏系统中模拟部分遮阳条件下提高性能.
- 为了确定最有效的计算和准确的算法,用于现实世界的应用.
主要方法:
- 实施和评估GWO,PSO,SSA和CS以优化在光伏系统中MPPT的ANN.
- 增强了带有扰动的数据集,以模拟现实的部分遮阳场景.
- 根据预测准确度 (平均平方误差 - MSE,平均绝对误差 - MAE) 和计算执行时间评估算法性能.
主要成果:
- 标准ANN表现不佳 (MSE:159.94,MAE:8.08) 的表现很差.
- GWO实现了最好的预测准确度 (MSE: 11.95,MAE: 2.46) 与良好的计算效率 (1199秒).
- SSA是最快的 (987秒) 具有竞争力的准确性 (MSE: 12.15,MAE: 2.70),而PSO最小化了MAE (2.17) 但更慢 (1418秒). CS显示出最不有利的结果.
结论:
- 灰狼优化器 (GWO) 为部分遮阳下的基于ANN的MPPT提供了预测准确度和计算速度之间的最佳平衡.
- 使用GWO的优化ANN显著提高了光伏系统中的能源采集效率.
- 这种方法对太阳能发电场和住宅太阳能电池板的现实应用具有前景,并有可能集成到智能电网系统中.
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