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相关概念视频

Bus Impedance Matrix01:24

Bus Impedance Matrix

144
Calculating subtransient fault currents for three-phase faults in an N-bus power system involves using the positive-sequence network. When a three-phase short circuit occurs at a specific bus, the analysis uses the superposition method to evaluate two separate circuits.
In the first circuit, all machine voltage sources are short-circuited, leaving only the prefault voltage source at the fault location. The positive-sequence bus impedance matrix can be determined by solving the nodal equations,...
144
Root-Locus Method01:19

Root-Locus Method

172
A cruise control system in a car is designed to maintain a specified speed automatically by adjusting the gas pedal. The system continuously measures the vehicle's speed and makes fine adjustments to the pedal to achieve this goal. The root locus method is particularly useful for understanding how the cruise control system's behavior changes under varying conditions, such as when the car goes uphill, downhill, or faces strong wind resistance.
This system can be represented by a block...
172
The Power Flow Problem and Solution01:26

The Power Flow Problem and Solution

253
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...
253
Fault Types01:18

Fault Types

100
When analyzing a single line-to-ground fault from phase A to ground at a three-phase bus, it is important to consider the fault impedance. This impedance is zero for a bolted fault, equal to the arc impedance for an arcing fault, and represents the total fault impedance for a transmission-line insulator flashover. To derive sequence and phase currents, fault conditions are translated from the phase domain to the sequence domain.
For line-to-line faults occurring between phases B and C, the...
100
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

136
Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...
136
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

266
The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
In this model, each generator is connected to a...
266

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相关实验视频

Updated: Jul 15, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

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基于相位空间重建方法的电动巴士踏板错误应用检测.

Aihong Lyu1, Kunchen Li2, Yali Zhang2

  • 1Vocational and Technical College, Xianyang Normal University, Xianyang 712000, China.

Sensors (Basel, Switzerland)
|September 28, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新型模型,用于检测电动巴士中的踏板误用,这是意外加速度的常见原因. 开发的系统可以准确地识别错误的踏板使用,提高电动巴士的安全性.

关键词:
深度神经网络是一个神经网络.电动公共汽车电动巴士踏板的错误应用 踏板的错误应用阶段空间重建阶段空间重建交通安全 交通安全 交通安全

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相关实验视频

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科学领域:

  • 运输工程 运输工程
  • 人工智能的人工智能
  • 车辆安全系统 车辆安全系统

背景情况:

  • 电动巴士越来越多地被采用,以获得环境效益,取代传统的燃料巴士.
  • 涉及电动汽车的事故往往与意外加速度 (UA) 有关,这通常是由于驾驶员踏板的错误操作造成的.
  • 现有的安全措施可能无法充分解决与电动巴士踏板误用相关的特定风险.

研究的目的:

  • 提出和验证一种用于检测电动巴士脚踏错误应用的模型 (MDPMEB).
  • 通过减轻意外加速带来的风险,提高电动巴士运行的安全性和可靠性.
  • 开发一个准确和高效的检测系统,用于正常制动与踏板错误应用事件.

主要方法:

  • 在受控环境中对城市电动巴士进行了自然驾驶和踏板误用模拟实验.
  • 应用基于混沌理论的相位空间重建,将序列踏板数据转换为高维图像数据集.
  • 开发了一个修改后的Swin变压器网络,在公共数据集上进行预训练,以提高概括性并防止小样本尺寸过度匹配.

主要成果:

  • 拟议的MDPMEB模型证明了准确和快速检测正常的制动和踏板的错误应用.
  • 在测试数据集上获得了97.58%的高准确率.
  • 在检测准确度方面,超过了传统的机器学习算法 (9.17%) 和卷积神经网络 (4.5%).

结论:

  • 在电动巴士中,MDPMEB模型有效地区分了正常制动和踏板错误应用.
  • 使用Swin变压器网络与基于混沌理论的数据映射相结合,为踏板误用检测提供了一个强大的解决方案.
  • 这项技术有可能显著提高电动巴士运输的安全性.