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相关概念视频

Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

180
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:
180
Energy Losses in Transformers01:21

Energy Losses in Transformers

852
In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
There are four main reasons for energy losses in transformers.
The first cause can be  the high resistance of the...
852
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
Heating and Cooling Curves02:44

Heating and Cooling Curves

22.8K
When a substance—isolated from its environment—is subjected to heat changes, corresponding changes in temperature and phase of the substance is observed; this is graphically represented by heating and cooling curves.
For instance, the addition of heat raises the temperature of a solid; the amount of heat absorbed depends on the heat capacity of the solid (q = mcsolidΔT). According to thermochemistry, the relation between the amount of heat absorbed or released by a substance, q, and its...
22.8K
Energy and Power Signals01:17

Energy and Power Signals

277
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:
277

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相关实验视频

Updated: Jun 18, 2025

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
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一个改进的短暂搜索优化算法用于建筑能效优化和混合能量化应用程序.

Thira Jearsiripongkul1, Mohammad Ali Karbasforoushha2, Mohammad Khajehzadeh3,4

  • 1Research Unit in Advanced Mechanics of Solids and Vibration, Department of Mechanical Engineering, Thammasat School of Engineering, Faculty of Engineering, Thammasat University, Pathumthani, 12121, Thailand. jthira@engr.tu.ac.th.

Scientific reports
|July 31, 2024
PubMed
概括

一个新的改进过渡搜索优化算法 (ITSOA) 提高了建筑物和混合系统的能源优化. 在基准测试和现实应用中,ITSOA的性能优于其他方法,证明了卓越的效率.

关键词:
建筑能效优化 建筑能效优化能源消耗 能源消耗是指能源的消耗.能源生产成本 能源生产成本的元启发式算法.暂时的搜索优化优化 暂时的搜索优化

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科学领域:

  • 计算智能是一种计算智能.
  • 优化算法 优化算法
  • 可持续能源系统 可持续能源系统

背景情况:

  • 传统的短暂搜索优化算法 (TSOA) 灵感来自电路动力学.
  • 现有的优化方法在平衡勘探和开采方面面临挑战.
  • 有效的优化对于降低建筑能耗和改进混合能源系统至关重要.

研究的目的:

  • 引入和评估改进的暂时搜索优化算法 (ITSOA).
  • 评估ITSOA在解决基准函数和优化建筑能源使用方面的有效性.
  • 验证ITSOA在优化混合能源系统生产方面的能力.

主要方法:

  • 通过将罗森布罗克的直接旋转技术集成到TSOA中,开发了改进的短暂搜索优化算法 (ITSOA).
  • 在23个经典基准函数上测试了ITSOA与传统TSOA和其他元启发算法 (DMO,SHO,GA,MRFO,PSO) 相比.
  • 应用ITSOA对单一和多目标建筑能源优化 (BEO) 问题,以尽量减少能源消耗.

主要成果:

  • 在解决基准函数方面,ITSOA表现优于传统的TSOA和其他比较方法.
  • 在单一和多目标建筑能源优化问题上,ITSOA实现了较低的成本函数值.
  • 在帕雷托前线内,ITSOA有效地确定了多目标BEO的最佳解决方案,使用模糊的决策方法.

结论:

  • 改进的暂时搜索优化算法 (ITSOA) 为复杂的优化任务提供了增强的功能.
  • 与现有的方法相比,ITSOA提供了一种更有效和更优化的建筑能源优化方法.
  • ITSOA显示出优化混合能源系统并为可持续能源解决方案做出贡献的巨大潜力.