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相关概念视频

Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

178
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:
178
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

104
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.
104
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
The Power Flow Problem and Solution01:26

The Power Flow Problem and Solution

183
Power flow problem analysis is fundamental for determining real and reactive power flows in network components, such as transmission lines, transformers, and loads. The power system's single-line diagram provides data on the bus, transmission line, and transformer. Each bus k in the system is characterized by four key variables: voltage magnitude Vk​, phase angle δk​, real power Pk​, and reactive power Qk​. Two of these four variables are inputs, while the...
183
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

636
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...
636
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

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相关实验视频

Updated: Jun 16, 2025

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
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混乱的自适应正弦共弦多目标优化算法,以解决微电网最佳能源调度问题.

N Karthik1, Arul Rajagopalan2, Mohit Bajaj3,4,5

  • 1Department of Electrical and Electronics Engineering, Hindustan Institute of Technology and Science, Chennai, Tamilnadu, India.

Scientific reports
|August 16, 2024
PubMed
概括
此摘要是机器生成的。

一个新的混乱自适应正弦共弦算法 (CSASCA) 优化了微电网调度,平衡成本和排放. 这种可再生能源解决方案比传统方法提供了更高的性能.

关键词:
能源管理 能源管理微电网 (MG) 是一个微电网.多目标优化多目标优化太阳能 (PV) 的光伏 (PV) 的可再生能源 (RES) 是一种可再生能源.sinus 和 cosine 的算法风力轮机 (WT) 的使用

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科学领域:

  • 可再生能源系统可再生能源系统
  • 优化算法 优化算法
  • 环境工程 环境工程

背景情况:

  • 越来越关注可再生能源的可靠性,效率和环境效益.
  • 微电网 (MG) 需要有效的调度,以平衡运营成本和排放.
  • 现有的优化方法在处理MGs中的多目标问题时面临挑战.

研究的目的:

  • 引入一个新的短期微型电网调度的多目标框架.
  • 开发混沌自适应正弦共弦算法 (CSASCA) 以尽量减少成本和污染.
  • 评估CSASCA在各种微电网运营场景中的表现.

主要方法:

  • 为微电网调度制定一个多目标优化问题的制定.
  • 开发和实施混乱自适应正弦共弦算法 (CSASCA).
  • 在优化过程中整合模糊逻辑,以加强决策.
  • 通过三个不同的运营场景进行绩效评估,并与传统的SCA进行比较.

主要成果:

  • 卡萨斯卡实现了优越的帕雷托最佳解决方案,有效平衡降低成本和减排.
  • 最佳值显示了成本 (例如,情景2中的98.203欧元) 和排放 (例如,情景1中的337.28公斤) 的显著改善.
  • 在勘探,融合,约束处理和参数灵敏度方面,CSASCA的表现优于传统的SCA.

结论:

  • CSASCA是解决微电网调度中的复杂多目标优化问题的强大而有效的工具.
  • 算法的混乱的自我适应机制显著提高了优化性能.
  • CSASCA为改善可再生能源系统的经济和环境效率提供了一个强大的解决方案.