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相关概念视频

Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
Maximum Power Transfer01:16

Maximum Power Transfer

Numerous practical applications within engineering disciplines, such as telecommunications, necessitate optimizing power delivery to a connected load. This pursuit, however, entails inherent internal losses, which can either equal or exceed the power supplied to the load. The Thevenin equivalent circuit is helpful in finding the maximum power a linear circuit can deliver to a load. It is assumed in this context that the load resistance can be adjusted.
By substituting the entire circuit with...
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
The Power Flow Problem and Solution01:26

The Power Flow Problem and Solution

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 power flow program computes the...
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
In this model, each generator is connected to a...
Wind Turbine Machine Models01:24

Wind Turbine Machine Models

In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
Induction machines interact through the rotating magnetic field generated by the stator and the rotor. The key parameter is slip, which is the difference between synchronous speed and rotor speed relative to synchronous speed. Slip is...

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相关实验视频

Updated: Jul 2, 2026

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

全面评估机器学习模型,以预测光伏系统的最大功率.

Samir A Hamad1, Mohamed A Ghalib1, Amr Munshi2

  • 1Process Control Technology Department, Faculty of Technology and Education, Beni-Suef University, Beni Suef, Egypt.

Scientific reports
|March 29, 2025
PubMed
概括

机器学习模型可以通过准确预测最大功率点 (MPP) 来优化独立的光伏 (PV) 系统. 决策树回归 (DTR) 在不同条件下预测光伏系统参数方面表现优异.

关键词:
人工神经网络的人工神经网络在DCDC转换器.机器学习就是机器学习.最大功率提取 (MPE) 技术预测模型的预测模型.

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Comparative Study of Simulation of Temperature Rise in Ring Main Unit
04:35

Comparative Study of Simulation of Temperature Rise in Ring Main Unit

Published on: July 5, 2024

相关实验视频

Last Updated: Jul 2, 2026

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

Comparative Study of Simulation of Temperature Rise in Ring Main Unit
04:35

Comparative Study of Simulation of Temperature Rise in Ring Main Unit

Published on: July 5, 2024

科学领域:

  • 可再生能源系统可再生能源系统
  • 机器学习应用 机器学习应用
  • 电力电子 电力电子 电力电子

背景情况:

  • 光伏 (PV) 系统表现出非线性发电,挑战有效的数据管理.
  • 波动的天气条件需要先进的方法来优化光伏系统的性能.
  • 机器学习 (ML) 提供了一种有前途的方法,通过跟踪其最大功率点 (MPP) 来提高光伏系统的效率.

研究的目的:

  • 开发和评估用于跟踪独立光伏系统的MPP的机器学习模型.
  • 为了比较各种ML算法在预测光伏系统参数中的预测精度.
  • 确定影响光伏系统优化ML模型性能的关键因素.

主要方法:

  • 探索ML算法,包括线性回归 (LR),回归 (RR),拉索回归 (Lasso R),贝叶斯回归 (BR),决策树回归 (DTR),梯度增强回归 (GBR) 和人工神经网络 (ANN).
  • 利用光伏单元的技术规格,辐射率和温度数据来预测最大功率,电流和电压.
  • 在100千瓦太阳能电池板上进行模拟;使用根平均平方误差 (RMSE),确定系数 (R2) 和平均绝对误差 (MAE) 评估性能.

主要成果:

  • 决策树回归 (DTR) 在预测最大电流 (Im),电压 (Vm) 和功率 (Pm) 方面明显优于其他经过测试的ML算法.
  • DTR模型实现了高精度,RMSE,MAE和R2值为Im的0.006,0.004和0.9999;Vm的0.015,0.0036和0.9999;Pm的2.36,0.871和0.9999;而Im的0.006,0.004和0.9999的RMSE,MAE和R2值为0.006和0.0099;Vm的0.015,0.0036和0.9999;Pm的2.36和0.871和0.9999的RMSE和MAE.
  • 功能重要性分析表明,培训数据集大小,操作条件,模型类型和数据预处理显著影响预测准确性.

结论:

  • 机器学习,特别是决策树回归,通过准确跟踪MPP,为优化独立光伏系统提供了有效的解决方案.
  • 开发的ML模型可以预测基本的光伏参数,并为提高转换器工作周期的调整提供信息,以改善能源采集.
  • 进一步的研究应考虑数据集大小,操作条件和预处理技术对光伏能源系统预测性能增强的影响.