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相关概念视频

Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

176
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
176
The Power Flow Problem and Solution01:26

The Power Flow Problem and Solution

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Power flow problem analysis is fundamental for determining real and reactive power flows in network components, such as transmission lines, transformers, and loads. The power system's single-line diagram provides data on the bus, transmission line, and transformer. Each bus k in the system is characterized by four key variables: voltage magnitude Vk​, phase angle δk​, real power Pk​, and reactive power Qk​. Two of these four variables are inputs, while the...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Proportional Integral (PI) controllers are a fundamental component in modern control systems, widely used to enhance performance and mitigate steady-state errors. They are particularly effective in applications such as automatic brightness adjustment on smartphones, where they excel at mitigating steady-state errors for step-function inputs. Unlike PD controllers, which require time-varying errors to function optimally, PI controllers leverage their integral component to address residual...
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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.

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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.

关键词:
一个年龄,一个年龄.在 CS CS CS 中,你会发现.这就是GWO GWO.在MPPTT中,MPPT是MPPT,MPPT是MPPT.公共服务人员 (PSO)这就是SSA SSA.生物启发的生物启发.优化算法优化算法

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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.