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Wind Turbine Machine Models01:24

Wind Turbine Machine Models

108
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...
108
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
Turbine-Governor Control01:17

Turbine-Governor Control

173
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...
173
Design Example: Calculating Safe Diameter for Wind-Exposed Disc01:17

Design Example: Calculating Safe Diameter for Wind-Exposed Disc

44
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...
44
Energy and Power Signals01:17

Energy and Power Signals

262
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:
262
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

1.4K
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
1.4K

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相关实验视频

Updated: Jun 9, 2025

Measurements of Waves in a Wind-wave Tank Under Steady and Time-varying Wind Forcing
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Measurements of Waves in a Wind-wave Tank Under Steady and Time-varying Wind Forcing

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短期风力发电间隔预测基于混合模式分解和改进的优化.

Jixuan Wang1, Yifan Tang1, Zengfu Xi1

  • 1College of Water Conservancy and Hydropower, Handan 056038, China.

Anais da Academia Brasileira de Ciencias
|October 23, 2024
PubMed
概括

本研究引入了一种混合方法,用于准确的风力发电间隔预测,改善电网稳定性和经济调度. 该方法将先进的分解技术与优化预测相结合,以实现可靠的能源管理.

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科学领域:

  • 可再生能源系统可再生能源系统
  • 电力系统运行 电力系统运行
  • 计算智能是一种计算智能.

背景情况:

  • 准确的风力发电预测对于电力系统的稳定性和经济调度至关重要.
  • 现有的方法经常与风力发电数据固有的波动性和非静止性作斗争.
  • 对于可靠的间隔预测来量化预测不确定性的需求至关重要.

研究的目的:

  • 开发一种混合方法,以提高风力发电间隔预测准确度.
  • 改进电力系统的经济调度和高峰频率调节.
  • 为风力发电预测提供可靠的不确定性量化.

主要方法:

  • 风力发电序列的分解使用改进的完整合体实证模式分解与适应噪声 (ICEEMDAN) 和变化模式分解.
  • 使用模糊透 (FE) 的分解元件的复杂性评估.
  • 混合预测利用组件智能预测,通过改进的子搜索算法 (ISSA) 优化超参数,以及使用内核密度估计 (KDE) 的间隔构建.

主要成果:

  • 在确定性预测中实现了2.8458MW和1.8605MW的根平均平方误差 (RMSE).
  • 在95%的信心水平下,高不确定性覆盖率为95.83%和97.92%.
  • 混合式方法有效地捕捉了风力发电的动态和不确定性.

结论:

  • 拟议的混合方法显著提高了风力发电间隔预测的准确性.
  • 这种方法有助于实现更稳定,更经济高效的电力系统运行.
  • 集成先进的分解,复杂性分析和优化为风力发电预测提供了一个强大的框架.