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相关概念视频

Multimachine Stability01:25

Multimachine Stability

150
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:
150
Load-frequency control01:28

Load-frequency control

140
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...
140
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

45
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...
45
SFG Algebra01:16

SFG Algebra

112
In Signal Flow Graph (SFG) algebra, the value a node represents is determined by the sum of all signals entering that node. This summed value is then transmitted through every branch leaving the node, making the SFG a powerful tool for visualizing and analyzing control systems.
Each node in an SFG corresponds to a variable, and the interactions between nodes are represented by branches with associated gains. When multiple branches lead into a node, the value at that node is the sum of the...
112

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相关实验视频

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通过改进的烟花算法优化和运行多能量合微电网 - - 混青跳跃算法.

Xubo Yue1, Jing Zhang2, Junhui Guo2

  • 1Taizhou Hongchuang Group, Taizhou, China.

PeerJ. Computer science
|August 15, 2024
PubMed
概括

本研究介绍了一种改进的烟花算法 (IFWA) 和混合的青跳跃算法 (SFLA),以优化多能微电网. IFWA-SFLA的方法提高了微电网的稳定性,能源流量管理,并减少了电压波动.

关键词:
结合的微电网是相连的改进了烟花演算法.多目标优化多目标优化混青跳跃算法 混青跳跃算法电压稳定性指数是电压稳定性的指数.

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科学领域:

  • 电气工程 电气工程
  • 可再生能源系统可再生能源系统
  • 优化算法 优化算法

背景情况:

  • 多能源合微电网面临着优化和运营挑战,影响系统稳定性和可靠性.
  • 综合性能源系统需要先进的解决方案,以实现高效的能源管理.
  • 现有的算法可能无法充分解决这些系统中复杂的相互依存关系.

研究的目的:

  • 提出和验证一个改进的烟花算法 (IFWA) 与混合的青跳跃算法 (SFLA) 集成,用于微电网优化.
  • 开发一个多目标优化模型,解决主动电网损失和静电压的问题.
  • 提高多能源合微电网的稳定性和可靠性.

主要方法:

  • 开发了一种改进的烟花算法 (IFWA),结合了适应性资源分配和社区遗传策略.
  • 制定了一个多目标优化模型,考虑了活性电源网络损失和静电压.
  • 混合青跳跃算法 (SFLA) 用于解决受约束的多目标优化问题.

主要成果:

  • IFWA-SFLA方法在优化微电网稳定性方面表现出卓越的表现.
  • 实现了微电网内的电能流的有效管理.
  • 通过模拟观察到电压波动的显著减少.

结论:

  • 拟议的IFWA-SFLA方法对于优化多能源合微电网是有效的.
  • 该方法通过提高稳定性和管理能源流量来提高系统可靠性.
  • 基于IFWA的微电网储能逆变器控制策略的进一步调查是有必要的.