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相关概念视频

Transformers in Distribution System01:27

Transformers in Distribution System

101
Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
101
Types Of Transformers01:16

Types Of Transformers

968
Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
968
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

149
In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
149
The Ideal Transformer01:26

The Ideal Transformer

378
In single-phase two-winding transformers, two windings are coiled around a magnetic core characterized by cross-sectional area A and magnetic permeability μ. A phasor current i1 enters the left winding while i2 exits the right winding, establishing the fundamental working of the transformer through electromagnetic principles.
Ampere's Law forms the basis of understanding the magnetic field within the transformer. It states that the integral of the magnetic field intensity's...
378
Energy Losses in Transformers01:21

Energy Losses in Transformers

862
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...
862
Transformers01:26

Transformers

1.1K
A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...
1.1K

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关于用变压器转换强化学习:发展轨迹

Shengchao Hu, Li Shen, Ya Zhang

    IEEE transactions on pattern analysis and machine intelligence
    |June 3, 2024
    PubMed
    概括

    变压器正在通过增强代理和环境建模来彻底改变强化学习 (RL). 本次调查探讨了基于变压器的RL (TRL) 进步,应用以及机器人技术和自动驾驶等领域的未来研究方向.

    科学领域:

    • 人工智能的人工智能
    • 机器学习 机器学习
    • 深度学习 (Deep Learning) 是一种深度学习.

    背景情况:

    • 变压器,最初用于NLP,在计算机视觉中显示出希望.
    • 它们强大的建模能力正在被用于强化学习 (RL).
    • 基于变压器的RL (TRL) 为智能代理提供了一个新的范式.

    研究的目的:

    • 调查和分析基于变压器的RL (TRL) 的最新进展.
    • 将TRL开发分类为架构增强和轨迹优化.
    • 确定TRL的关键应用和未来的研究趋势.

    主要方法:

    • 剖析最近关于TRL的文献.
    • 将TRL方法分类为架构增强和轨迹优化.
    • 检查机器人,基于文本的游戏,导航和自动驾驶中的TRL应用.

    主要成果:

    • 架构增强改进RL代理/环境建模,但面临着传统RL的局限性.
    • 轨迹优化方法利用变压器进行序列建模和从数据集中提取策略.
    • 从操纵到自主系统,TRL在各种应用中展示了潜力.

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    结论:

    • 与传统的深度RL技术相比,TRL提供了显著的进步.
    • 未来的研究应该解决TRL的局限性,并探索新的应用.
    • 这项调查为TRL研究提供了全面的概述和路线图.