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相关概念视频

Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

148
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:
148
Multimachine Stability01:25

Multimachine Stability

129
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:
129
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

91
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.
91
Power System Distribution01:25

Power System Distribution

224
Power system distribution involves delivering electrical energy from power plants to consumers through a network of transmission and distribution systems. The process begins at power plants, where energy from coal, gas, nuclear, water, and wind is converted into electrical energy. These plants use three-phase generators, typically rated between 50 to 1300 MVA, with terminal voltages ranging from a few kV to 20 kV, depending on the size and age of the units.
The transmission system is designed...
224
Power System Three-Phase Short Circuits01:21

Power System Three-Phase Short Circuits

71
Determining the subtransient fault current in a power system involves representing transformers by their leakage reactances, transmission lines by their equivalent series reactances, and synchronous machines as constant voltage sources behind their subtransient reactances. In this analysis, certain elements are excluded, such as winding resistances, series resistances, shunt admittances, delta-Y phase shifts, armature resistance, saturation, saliency, non-rotating impedance loads, and small...
71
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

170
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...
170

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Updated: May 23, 2025

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统一的弹性模型使用深度学习来评估电力系统性能.

Volodymyr Artemchuk1,2,3,4, Iurii Garbuz1, Jamil Abedalrahim Jamil Alsayaydeh5

  • 1Department of Mathematical and Econometric Modelling, G.E. Pukhov Institute for Modelling in Energy Engineering of the NAS of Ukraine, Kyiv, Ukraine.

Heliyon
|March 11, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了使用深度学习 (DL) 改进可再生能源系统的统一弹性模型 (URM). 该模型增强了电池和逆变器对环境因素的弹性,提高了电力系统的性能.

关键词:
深度学习是一种深度学习.能源弹性 能源弹性忠诚度 忠诚度 忠诚度天气影响影响天气影响

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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
  • 环境科学 环境科学

背景情况:

  • 电池和逆变器等可再生能源组件的能源弹性对于可靠的电力系统至关重要.
  • 环境因素对电力系统的性能和运行忠实性产生重大影响.
  • 现有的模型可能无法完全捕捉影响能源弹性因素的复杂相互作用.

研究的目的:

  • 引入一种新的统一弹性模型 (URM),利用深度学习 (DL) 来提高电力系统性能.
  • 分析和量化环境因素对储能设备,特别是电池的弹性影响.
  • 开发一种以数据为导向的方法,以提高可再生能源传播组件的运行准确性.

主要方法:

  • 基于深度学习 (DL) 算法的统一弹性模型 (URM) 的开发.
  • 对影响电池和储能系统弹性环境因素的分析.
  • 使用低弹性排水事件的历史数据来训练DL模型.
  • 利用模型输出来增强强化因素并减轻性能流失.

主要成果:

  • URM有效地分析了对电池和逆变器弹性的环境影响.
  • DL方法成功地训练了低弹性数据,以预测和提高性能.
  • 综合的排水减排和性能增强策略验证了该模型的有效性.
  • 在电力系统运行忠实性方面取得了显著的改进,特别是在天气影响方面.

结论:

  • 统一的弹性模型 (URM) 提供了一个强大的框架,用于提高可再生能源系统的能源弹性.
  • 深度学习为分析复杂的环境相互作用和提高电力系统性能提供了强大的工具.
  • 该模型的验证证实了其能够减轻性能耗尽并提高运行忠实性的能力,特别是在恶劣的天气条件下.