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相关概念视频

Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

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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:
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Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

593
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.
593
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

2.8K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
2.8K
The Power Flow Problem and Solution01:26

The Power Flow Problem and Solution

839
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 power flow program computes...
839
Power Factor Correction01:20

Power Factor Correction

487
The power transmission to a factory involves the transfer of apparent power, a combination of active and reactive power. The power factor measures how effectively electrical power is converted into useful work output. The ratio of the real power (KW) that does the work to the apparent power (KVA) supplied to the circuit.
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Maximum Power Transfer01:16

Maximum Power Transfer

828
Numerous practical applications within engineering disciplines, such as telecommunications, necessitate optimizing power delivery to a connected load. This pursuit, however, entails inherent internal losses, which can either equal or exceed the power supplied to the load. The Thevenin equivalent circuit is helpful in finding the maximum power a linear circuit can deliver to a load. It is assumed in this context that the load resistance can be adjusted.
By substituting the entire circuit with...
828

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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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基于改进的斑马优化算法 - 随机配置网络的短期光伏电力预测

Yonggang Wang1, Wenpeng Li1, Haoran Chen1

  • 1School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China.

Sensors (Basel, Switzerland)
|September 19, 2025
PubMed
概括

本研究引入了一种改进的光伏发电预测模型,提高了电网稳定性. 这种新的方法显著提高了在不同天气条件下短期光伏发电量预测的准确性.

关键词:
光伏发电是光伏发电的重要组成部分.短期光伏电力预测随机配置 网络网络的随机配置斑马优化算法 斑马优化算法

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科学领域:

  • 可再生能源系统可再生能源系统
  • 人工智能在电力工程中的应用
  • 计算智能是一种计算智能.

背景情况:

  • 由于天气的变化,光伏 (PV) 发电输出本质上是不确定的,这对电网稳定性构成了挑战.
  • 准确的短期光伏电力预测对于减轻电网干扰和确保可靠的电力供应至关重要.

研究的目的:

  • 开发一个先进的短期光伏电力预测模型.
  • 通过解决与天气有关的不确定性,提高光伏功率预测的准确性和可靠性.

主要方法:

  • 开发了一种新的预测模型,将改进的斑马优化算法 (IZOA) 与随机配置网络 (SCN) 结合起来.
  • 历史光伏数据被分为三个不同的天气模式,以减少输出不确定性.
  • 使用IZOA优化SCN的关键参数,增强其预测能力.

主要成果:

  • 拟议的IZOA-SCN模型显著提高了短期光伏发电量预测准确度.
  • 该方法有效地处理了不同天气模式的功率输出变化.
  • 实验结果证实了IZOA-SCN模型的性能优于现有的预测方法.

结论:

  • 开发的IZOA-SCN模型为短期光伏电力预测提供了强大而准确的解决方案.
  • 这种方法通过提供可靠的光伏功率预测,有助于提高电网稳定性.
  • 该研究强调了将优化算法与先进的神经网络相结合的有效性,用于可再生能源预测.