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相关概念视频

Multimachine Stability01:25

Multimachine Stability

153
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
153
Load-frequency control01:28

Load-frequency control

165
Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
165
Load along a Single Axis01:29

Load along a Single Axis

304
In structural engineering, the analysis of beams subjected to varying loads is a critical aspect of understanding the behavior and performance of these structural elements. A common scenario involves a beam subjected to a combination of different load distributions.
Consider a beam of length L subjected to a varying load, which is a combination of parabolic and trapezoidal load distribution along the x-axis. In this case, it is essential to determine the resultant loads, their locations, and...
304
Distributed Loads01:19

Distributed Loads

538
Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
538
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

229
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...
229
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

54
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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相关实验视频

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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一个ADMM-LSTM框架用于短期负载预测.

Shuo Liu1, Zhengmin Kong1, Tao Huang2

  • 1School of Electrical Engineering and Automation, Wuhan University, Wuhan, 430072, China.

Neural networks : the official journal of the International Neural Network Society
|February 8, 2024
PubMed
概括

本研究介绍了ADMM-LSTM,这是一个优化的短期负载预测 (STLF) 框架,可以克服传统方法的局限性. 通过利用乘数器的交替方向方法来训练长期短期记忆网络,ADMM-LSTM提高了电力系统操作的准确性和效率.

关键词:
乘数器的交替方向方法没有梯度的功能.长期短期内存网络中的长期内存.短期负载预测 短期负载预测

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科学领域:

  • 电气工程 电气工程
  • 计算机科学 计算机科学
  • 人工智能的人工智能

背景情况:

  • 准确的短期负载预测 (STLF) 对电力系统的可靠性和效率至关重要.
  • 随着可再生能源和电动汽车数据复杂性的增加,需要先进的预测方法.
  • 传统的长期短期记忆 (LSTM) 训练方法面临着爆炸/消失梯度等局限性.

研究的目的:

  • 为STLF提供一个创新的LSTM优化框架,ADMM-LSTM.
  • 解决LSTM传统的基于随机梯度的训练方法的局限性.
  • 为了提高STLF的准确性和计算效率.

主要方法:

  • 开发了ADMM-LSTM,这是一个用于LSTM的分布式培训框架,使用了乘数 (ADMM) 的交替方向方法.
  • 引入了一种新的向后向前参数更新顺序,以减少计算时间.
  • 用近点算法或局部线性近似来解决子问题,避免外部解决者.
  • 提供了ADMM-LSTM的收性质的理论分析.

主要成果:

  • ADMM-LSTM有效地以分布式的方式训练LSTM网络.
  • 后向前向更新顺序显著减少了计算时间.
  • 该框架本质上避免了爆炸或消失的梯度问题.
  • 公共数据集的实验结果显示,与现有的STLF方法相比,其性能优越.

结论:

  • ADMM-LSTM为STLF提供了一个强大的,高效的替代方案.
  • 拟议的优化框架提高了LSTM在电力系统应用中的性能.
  • 无梯度性质和收性质使ADMM-LSTM成为复杂预测任务的宝贵工具.