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相关概念视频

Transformers in Distribution System01:27

Transformers in Distribution System

494
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...
494
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

517
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...
517
Transformers01:26

Transformers

1.7K
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...
1.7K
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

428
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
428
Energy Losses in Transformers01:21

Energy Losses in Transformers

1.3K
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...
1.3K
Types Of Transformers01:16

Types Of Transformers

1.4K
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...
1.4K

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相关实验视频

充电站使用基于LSTM的混合变压器模型进行需求预测.

Adil Hussain1, Vishwanath Eswarakrishnan2, Ayesha Aslam3

  • 1School of Electronics and Control Engineering, Chang'an University, Xi'an, 710000, China. 2022032907@chd.edu.cn.

Scientific reports
|October 21, 2025
PubMed
概括

准确的电动汽车 (EV) 充电需求预测对于电网稳定至关重要. 一个新的LSTM-变压器模型显著改善了中长期电动汽车充电需求预测,优于传统方法.

关键词:
收费需求的收费方式深度学习是一种深度学习.需求预测需要预测.电动汽车 电动汽车是什么混合变压器 混合变压器

相关实验视频

科学领域:

  • 电气工程 电气工程
  • 人工智能的人工智能
  • 数据科学数据科学数据科学

背景情况:

  • 准确的电动汽车 (EV) 能源需求预测对于电力系统稳定性和充电站可靠运行至关重要.
  • 根据历史数据分析充电需求模式,中长期预测至关重要.

研究的目的:

  • 提出和评估一种基于LSTM的新型编码解码器变压器模型,用于预测电动汽车充电站 (EVCS) 需求.
  • 使用现实数据集,将拟议模型的性能与传统的LSTM和变压器模型进行比较.

主要方法:

  • 开发了一个混合LSTM-变压器模型,集成了一个基于LSTM的编码器-解码器架构.
  • 在ACN的开放数据集上训练和测试该模型,特别是Caltech和JPL的充电数据.
  • 使用平均绝对误差 (MAE) 和平均平方误差 (MSE) 预测未来30,120和240天的需求预测的评估性能.

主要成果:

  • 对于加州理工学院和JPL数据集来说,LSTM-Transformer模型显示了与基线模型相比的显著改进.
  • 对于加州理工学院的数据,MAE和MSE在30天的时间里分别减少了17.27%和19.79%.
  • 对于JPL数据,MAE和MSE的减少在30天后达到24.91%和23.17%,在更长的时间内有持续的改善.

结论:

  • 拟议的LSTM-变压器模型有效地提高了中长期电动汽车充电需求预测的准确性.
  • 混合模型的性能优于传统的深度学习方法,为电力系统管理和充电基础设施规划提供更可靠的预测.