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Multimachine Stability01:25

Multimachine Stability

197
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:
197
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

285
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...
285
Wind Turbine Machine Models01:24

Wind Turbine Machine Models

170
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...
170
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

678
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
678
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

138
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.
138
Distributed Loads01:19

Distributed Loads

558
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...
558

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相关实验视频

Updated: Jul 24, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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一个机器学习模型组合用于跨多个时间视界的混合功率负载预测.

Nikolaos Giamarelos1, Myron Papadimitrakis1, Marios Stogiannos1

  • 1Department of Electrical and Electronic Engineering, University of West Attica, Thivon 250, 122 41 Aigaleo, Greece.

Sensors (Basel, Switzerland)
|July 8, 2023
PubMed
概括

本研究引入了一种使用多个机器学习模型的新智能电网负载预测方法. 整体方法可以准确地预测前24小时的电荷,从而改善电网管理.

关键词:
组合学习组合学习负载预测负载预测神经网络的神经网络的神经网络稀疏的代表性 稀疏的代表性支持向量的回归.

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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
  • 数据科学数据科学数据科学

背景情况:

  • 向可再生能源的转变需要先进的智能电网技术.
  • 准确的负载预测对于高效的电力系统规划,运行和管理至关重要.
  • 传统的电网模型正在向智能电网框架发展.

研究的目的:

  • 开发一个新的混合组合预测方案,用于电荷.
  • 从15分钟到24小时的多个时间地平线提供准确的预测.
  • 提高智能电网运营的可靠性和效率.

主要方法:

  • 利用了一组多样化的机器学习模型:神经网络,线性回归,支持向量回归,随机森林和稀疏回归.
  • 实施了在线决策机制,以动态权衡最终预测的个别模型性能.
  • 通过使用来自高压/中压变电站的现实电荷数据来评估该方案.

主要成果:

  • 实现了高精度,R平方值从0.99 (提前15分钟) 到0.79 (提前24小时).
  • 与最先进的机器学习方法和其他组合技术相比,已证明的优越或竞争性性能.
  • 验证了拟议的混合组合预测方案在实际网格数据上的有效性.

结论:

  • 拟议的混合组合负载预测方案对智能电网非常有效.
  • 该方法为各种短期和中期地平线提供了可靠和准确的预测.
  • 这种方法有助于在不断变化的电力系统中改进规划和运营效率.