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Related Concept Videos

Power System Three-Phase Short Circuits01:21

Power System Three-Phase Short Circuits

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Determining the subtransient fault current in a power system involves representing transformers by their leakage reactances, transmission lines by their equivalent series reactances, and synchronous machines as constant voltage sources behind their subtransient reactances. In this analysis, certain elements are excluded, such as winding resistances, series resistances, shunt admittances, delta-Y phase shifts, armature resistance, saturation, saliency, non-rotating impedance loads, and small...
207
Bus Impedance Matrix01:24

Bus Impedance Matrix

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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.
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,...
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Three-Phase Short Circuit—Unloaded Synchronous Machine01:21

Three-Phase Short Circuit—Unloaded Synchronous Machine

327
Conducting a three-phase short circuit test on an unloaded synchronous machine helps understand its impact on the system. The AC fault current's oscillogram, with the DC offset removed, reveals that the waveform amplitude decreases from an initially high value to a steady-state level for one phase of the machine.
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Fault Types01:18

Fault Types

180
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...
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Determination of Expected Frequency01:08

Determination of Expected Frequency

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Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
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Series Impedances: Three-Phase Line01:27

Series Impedances: Three-Phase Line

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Calculating series impedances for a three-phase overhead line involves evaluating resistances and inductive reactances in a network with three-phase and multiple neutral conductors grounded at regular intervals.
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Related Experiment Video

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Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
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Using empirical traffic trajectory data for crash risk evaluation under three-phase traffic theory framework.

Tong Liu1, Zhibin Li1, Pan Liu1

  • 1School of Transportation, Southeast University, Nanjing, 210000, China.

Accident; Analysis and Prevention
|May 20, 2021
PubMed
Summary

This study analyzed traffic phases and crash risks using vehicle data. Synchronized flow and wide moving jams pose the highest risks, highlighting the need to integrate traffic phases and parameters for better safety predictions.

Keywords:
FreewaySurrogate safety measureThree phase traffic theoryTraffic flowTraffic state

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Area of Science:

  • Traffic Engineering
  • Road Safety Analysis
  • Transportation Systems

Background:

  • Understanding traffic dynamics is crucial for road safety.
  • The three-phase traffic theory categorizes traffic flow into distinct states.
  • Microscopic analysis of vehicle trajectories offers detailed insights into crash risks.

Purpose of the Study:

  • To evaluate crash risks across different traffic phases and transitions using surrogate safety measures.
  • To investigate the influence of traffic flow variables (flow rate, density, speed) on crash risks.
  • To develop predictive models for crash risk based on traffic states and parameters.

Main Methods:

  • Utilized empirical vehicle trajectory data from highways in California, USA, and Nanjing, China.
  • Identified traffic phases based on traffic flow variables and their correlations.
  • Applied advanced crash risk indexes derived from vehicle trajectories.
  • Developed regression models to quantify the relationship between traffic variables, states, and crash risks.

Main Results:

  • Significant variations in safety performance were observed across different traffic states.
  • Synchronized flow and wide moving jam phases were identified as the most hazardous.
  • High traffic density and low speeds correlate strongly with increased crash risk.
  • Integrating traffic phases and parameters improved crash risk prediction accuracy.

Conclusions:

  • Traffic phase and its characteristics are critical determinants of road safety.
  • Specific traffic states like synchronized flow and wide moving jams require targeted safety interventions.
  • Microscopic analysis and advanced risk indexes provide valuable tools for traffic safety assessment and management.