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相关概念视频

Wind Turbine Machine Models01:24

Wind Turbine Machine Models

90
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...
90
Turbine-Governor Control01:17

Turbine-Governor Control

117
Turbine-governor control is crucial for maintaining power system stability by balancing turbine mechanical power output with electrical load demand. This mechanism ensures that generator frequency and rotor speed are within acceptable limits during load variations. Turbine-generator units store kinetic energy due to their rotating masses; this energy is released to meet the load requirement when the load increases. The electrical torque of turbines rises to meet the demand, whereas the...
117
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

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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.
88
Energy and Power Signals01:17

Energy and Power Signals

215
In an electrical system with a resistor, voltage and current signals facilitate the measurement of power and energy across the resistor. For a continuous-time signal, the total energy over a time interval is defined as the integral of the square of the signal's magnitude over that interval. Mathematically, this is expressed as:
215
Design Example: Calculating Safe Diameter for Wind-Exposed Disc01:17

Design Example: Calculating Safe Diameter for Wind-Exposed Disc

14
Assessing safety in wind-exposed installations is crucial to preventing potential failures. This example explores the calculation and design adjustments needed to mount a circular disc on a building facade, where wind forces are a primary concern. A 4-meter diameter disc was initially designed as an aesthetic feature facing winds at a velocity of 25 meters per second, with an air density of 1.25 kilograms per cubic meter. Given these conditions, the drag force on the disc was determined using...
14
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

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Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
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相关实验视频

Updated: May 12, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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嵌入了数据转换和多目标特征选择算法的增强框架,用于预测风力发电.

Yahya Z Alharthi1, Haruna Chiroma2, Lubna A Gabralla3

  • 1Department of Electrical Engineering, College of Engineering, University of Hafr Albatin, 39524, Hafr Al Batin, Saudi Arabia. yalharthi@uhb.edu.sa.

Scientific reports
|May 9, 2025
PubMed
概括
此摘要是机器生成的。

准确的风能预测对于管理可再生能源至关重要. 这项研究引入了使用特征选择和混合深度循环网络长期短期存储模型的新框架,以显著提高风力发电预测的准确性.

关键词:
数据转换 - 数据转换.深度循环神经网络深度循环神经网络功能选择 功能选择长期的短期记忆 长期的短期记忆可再生能源是可再生的能源.风力轮机发电的动力是风力轮机的动力.

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科学领域:

  • 可再生能源系统可再生能源系统
  • 人工智能在能源中的作用
  • 计算智能是一种计算智能.

背景情况:

  • 全球对风能的兴趣需要精确的风能预测,以便有效地管理电网.
  • 现有的预测方法经常与复杂的,高维的风能数据集作斗争.
  • 优化特征选择和数据处理是提高预测模型性能的关键.

研究的目的:

  • 为准确的风力发电预测提出一个综合框架.
  • 为了利用先进的算法进行最佳的功能选择和数据转换.
  • 为风力发电预测开发一个强大的混合深度循环网络长期短期存储模型.

主要方法:

  • 利用多目标无主导排序基因算法III (NSGA-III) 来从风能数据中进行最佳特征选择.
  • 在模型输入之前对选定的特征实施数据转换机制.
  • 开发并应用了混合深度循环网络 (DRN) 和长短期内存 (LSTM) 架构用于风力发电建模.

主要成果:

  • 与传统方法相比,拟议的框架显示出更高的有效性和稳定性.
  • 实现了2.6593e-10的显著较低的平均平方误差 (MSE) 和1.630e-05.5的根平均平方误差 (RMSE).
  • 优于现有的方法,突出了综合功能选择和混合深度学习方法的好处.

结论:

  • 将数据转换机制与NSGA-III和混合DRN-LSTM模型集成,可以大大提高风力发电预测的准确性.
  • 拟议的框架为管理风能发电提供了强大而有效的解决方案.
  • 这项研究提供了宝贵的见解和未来研究先进的风能预测技术的基础.