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相关概念视频

Wind Turbine Machine Models01:24

Wind Turbine Machine Models

92
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...
92
Turbine-Governor Control01:17

Turbine-Governor Control

124
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...
124
Generation of Three-Phase Voltage01:21

Generation of Three-Phase Voltage

326
A three-phase AC generator has a rotor with a rotating magnet placed within the stator mounted with the stationary three-phase winding to generate three-phase voltages via mutual induction. These windings are evenly distributed around the inner circumference of the stator and are arranged 120 electrical degrees apart. Three-phase stator windings consist of three separate coils or groups of coils, known as phases, each connected in Y (star) configuration or Delta configuration.
As the rotor...
326
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

194
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
194
Survival Tree01:19

Survival Tree

44
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
44
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

139
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:
139

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使用堆叠和转移学习进行风能预测.

Xu Cheng1, Yu Cao2, Zhiyuan Song3

  • 1College of Economics and Management, Shenyang Agricultural University, Shenyang, 110886, China.

Scientific reports
|April 4, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种用于超短期风力发电预测的先进方法,该方法结合了堆积和转移学习. 新的方法显著提高了预测准确性,与个人模型相比,用于更好的电网管理.

关键词:
长期短期记忆 长期短期记忆主要组件分析的主要组件分析.堆叠组合模型的模型.转移学习转移学习超短时间预测超短时间预测.风力发电是风力发电的重要组成部分.

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科学领域:

  • 可再生能源可再生能源是可再生能源.
  • 电气工程 电气工程
  • 数据科学数据科学数据科学

背景情况:

  • 精确的风力发电预测对于电网稳定性和经济效率至关重要,因为可再生能源的采用量增加.
  • 现有的预测方法在实现超短期风力发电输出的高精度方面面临挑战.

研究的目的:

  • 开发和评估一种新的组合方法,以提高超短期风力发电预测的准确性.
  • 利用堆积和转移学习技术来提高预测性能.

主要方法:

  • 使用主要组件分析 (PCA) 减少数据的维度.
  • 使用长短期记忆 (LSTM),双向LSTM (BiLSTM),门式循环单元 (GRU),双向GRU (BiGRU) 和LSTM-Attention作为基础预测模型.
  • 采用堆叠组合来结合基准模型的预测.
  • 实施转移学习以提高模型的概括性和性能.

主要成果:

  • 拟议的堆叠和转移学习方法在与单个基准模型相比,在超短时间的风力发电预测中表现出更高的准确性.
  • PCA有效地减少了数据尺寸,有助于模型效率.
  • 整体方法成功地整合了各种深度学习模型,以实现可靠的预测.

结论:

  • 堆叠和转移学习的结合方法在超短期风力发电预测方面取得了重大进展.
  • 这种方法为电网运营商提供了更可靠的工具来管理波动的风能资源.
  • 未来的工作可以探索额外的功能工程和超参数优化,以进一步改进.