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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.
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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.
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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...
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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.
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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.
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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
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Electric Bus Pedal Misapplication Detection Based on Phase Space Reconstruction Method.

Aihong Lyu1, Kunchen Li2, Yali Zhang2

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

Sensors (Basel, Switzerland)
|September 28, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel model to detect pedal misapplication in electric buses, a common cause of unintended acceleration. The developed system accurately identifies incorrect pedal use, enhancing electric bus safety.

Keywords:
deep neural networkselectric busespedal misapplicationphase space reconstructiontraffic safety

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Area of Science:

  • Transportation Engineering
  • Artificial Intelligence
  • Vehicle Safety Systems

Background:

  • Electric buses are increasingly adopted for environmental benefits, replacing traditional fuel buses.
  • Accidents involving electric vehicles are often linked to unintended acceleration (UA), frequently caused by driver pedal misapplication.
  • Existing safety measures may not adequately address the specific risks associated with pedal misapplication in electric buses.

Purpose of the Study:

  • To propose and validate a Model for Detecting Pedal Misapplication in Electric Buses (MDPMEB).
  • To enhance the safety and reliability of electric bus operations by mitigating risks from unintended acceleration.
  • To develop an accurate and efficient detection system for normal braking versus pedal misapplication events.

Main Methods:

  • Conducted natural driving and pedal misapplication simulation experiments with urban electric buses in a controlled environment.
  • Applied phase space reconstruction based on chaos theory to transform sequential pedal data into high-dimensional image datasets.
  • Developed a modified Swin Transformer network, pre-trained on a public dataset to improve generalization and prevent overfitting with small sample sizes.

Main Results:

  • The proposed MDPMEB model demonstrated accurate and rapid detection of normal braking and pedal misapplication.
  • Achieved a high accuracy rate of 97.58% on the test dataset.
  • Outperformed traditional machine learning algorithms (by 9.17%) and Convolutional Neural Networks (by 4.5%) in detection accuracy.

Conclusions:

  • The MDPMEB model is effective in distinguishing between normal braking and pedal misapplication in electric buses.
  • The use of Swin Transformer networks combined with chaos theory-based data mapping offers a robust solution for pedal misapplication detection.
  • This technology has the potential to significantly improve the safety of electric bus transportation.