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相关概念视频

Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

578
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.
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Energy and Power Signals01:17

Energy and Power Signals

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In an electrical system with a resistor, voltage and current signals facilitate the measurement of power and energy across the resistor. For a continuous-time signal, the total energy over a time interval is defined as the integral of the square of the signal's magnitude over that interval. Mathematically, this is expressed as:
1.0K
Electrical Energy01:10

Electrical Energy

1.6K
Using electric appliances for a longer period of time consumes more electrical energy and results in a higher electric bill. The energy produced by the transfer of electrons from one point to another is known as electrical energy. If power is delivered at a constant rate, the electrical energy can be defined as the product of power used by the device for a period of time. The energy unit on electric bills is the kilowatt-hour, where one kilowatt-hour is equivalent to 3.6 × 106 joules.
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Maximum Power Transfer01:16

Maximum Power Transfer

811
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...
811
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

1.1K
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of the...
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P-N junction01:11

P-N junction

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A p-n junction is formed when p-type and n-type semiconductor materials are joined together. At the interface of the p-n junction, holes from the p-side and electrons from the n-side begin to diffuse into the opposite sides due to the concentration gradient. This diffusion of carriers leads to a region around the junction where there are no free charge carriers, known as the depletion region. The charge density within the depletion region for the n-side and p-side can be described by the...
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相关实验视频

Updated: Jan 11, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

999

智能城市光伏照明系统的优化能源管理,使用香水优化算法和图集神经网络的神经网络.

Zakir Hussain1, Prabu Selvam2, M Sivaramkrishnan3

  • 1Department of Chemical Technology, Loyola Academy, Secunderabad, 500015, Telangana, India.

Scientific reports
|November 17, 2025
PubMed
概括

本研究介绍了智能城市光伏照明系统中能源管理的混合战略,将预测优化和神经网络集成在一起,以降低成本和提高效率. 新方法显著提高了运营成本效益和系统性能.

关键词:
和风力轮机的风力轮机.能源管理控制器控制器能源管理系统 能源管理系统网格格子 网格格子 网格格子太阳能光伏系统的使用智能家居是一个智能家居.智能电表是一个智能电表.

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科学领域:

  • 可再生能源系统可再生能源系统
  • 智慧城市基础设施 智慧城市基础设施
  • 能源管理 能源管理

背景情况:

  • 智能城市越来越多地集成可再生能源 (RES),如光伏电池板和风力轮机 (WT) 用于照明.
  • 由于供需波动,在这些系统中平衡成本和效率是具有挑战性的,需要有效的能源存储 (ESS) 和电网集成.
  • 优化能源利用和减少对传统能源的依赖是可持续城市发展的关键目标.

研究的目的:

  • 为智慧城市的光伏供电照明系统提出混合能源管理 (EM) 战略.
  • 为了降低运营成本,提高这些系统的能源效率.
  • 为了应对能源供应和需求与日益增长的可再生能源整合相匹配的挑战.

主要方法:

  • 开发了一种混合战略,集成预测优化算法 (POA) 和生成神经网络 (GENN).
  • POA优化了可再生能源,电网和ESS之间的能源分配,以实现资源利用和供需平衡.
  • GENN提高了对能源生产和消费模式的预测准确度.

主要成果:

  • 拟议的POA-GENN方法在MATLAB平台上与现有方法进行了实施和评估.
  • 该系统实现了非常低的运营成本,为365.24欧元.
  • 证明了99.2%的卓越能源效率,验证了该方法的有效性.

结论:

  • 混合POA-GENN战略有效地优化了光伏驱动智慧城市照明中的能源管理.
  • 这种方法显著提高了复杂的城市能源系统的成本效益和能源利用率.
  • 调查结果强调了先进的新兴市场战略的潜力,以支持可持续的智慧城市发展.