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The Power Flow Problem and Solution01:26

The Power Flow Problem and Solution

253
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...
253
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

229
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:
229
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

131
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.
131
Control of Power Flow01:30

Control of Power Flow

283
There are several methods to control power flow in power systems:
283
Load-frequency control01:28

Load-frequency control

181
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...
181
Multimachine Stability01:25

Multimachine Stability

180
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:
180

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相关实验视频

Updated: Jul 15, 2025

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

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一种可调节的Predictive&Prescriptive方法用于基于RO的最佳功率流量问题.

Liqin Zheng1,2, Xiaoqing Bai1, Xiaoqing Shi1

  • 1Guangxi Key Laboratory of Power System Optimization and Energy Technology, College of Electrical Engineering, Guangxi University, Nanning 530004, China.

Heliyon
|October 2, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种可调节的框架,将机器学习和强大的优化 (RO) 结合起来,以改善考虑不确定性 (OPF-U) 问题的最佳功率流. 与现有方法相比,新方法提供了更经济,更强大的解决方案.

关键词:
机器学习 机器学习最佳的功率流量是最佳的.两个阶段的强大的优化优化.不确定波动区域是不确定的波动区域.

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相关实验视频

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科学领域:

  • 电气工程 电气工程
  • 运营研究 运营研究
  • 数据科学数据科学数据科学

背景情况:

  • 传统的考虑不确定性的最佳功率流 (OPF-U) 依赖于预测值,但预测错误对规范分析的影响尚不清楚.
  • 现有的OPF-U方法通常使用统计或机器学习进行预测,然后进行强大的优化 (RO).

研究的目的:

  • 为OPF-U问题提出一个结合机器学习和RO的可调节框架,以解释预测错误.
  • 开发一个强大的波动区域,具有可调节的参数,以改善OPF-U的不确定性处理.

主要方法:

  • 利用k-最近邻居 (k-NN) 来识别围绕预测不确定性值的样本.
  • 从k-NN样本构建了一个最小体积圆体 (MVE) 集 (KMV集).
  • 开发了一个可调节的强波动区域,使用KMV集的两项指数公式.
  • 将波动区域嵌入到两阶段RO模型中,以解决OPF-U问题.

主要成果:

  • 与最先进的盒子和圆形集相比,拟定的波动区域表现出优越的稳定性和可调性.
  • 两个阶段的RO模型比现有的RO模型提供了更经济的解决方案.
  • 样本之外的模拟证实,通过拟议的可调节的预测和规范方法,较大系统的计算负担降低.

结论:

  • 拟议的可调节框架有效地整合了OPF-U的预测和规范分析.
  • 该方法提供了一种更强大,更经济,更高效的计算方法来处理电流优化的不确定性.