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Related Concept Videos

Multimachine Stability01:25

Multimachine Stability

600
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
600
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...
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Three-Phase Short Circuit—Unloaded Synchronous Machine01:21

Three-Phase Short Circuit—Unloaded Synchronous Machine

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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.
This behavior occurs due to the magnetic flux produced by the short-circuit armature currents. Initially, these currents follow high-reluctance paths but eventually shift to...
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Fault Types01:18

Fault Types

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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.
For line-to-line faults occurring between phases B and C, the...
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Bus Impedance Matrix01:24

Bus Impedance Matrix

551
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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Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

841
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...
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Related Experiment Video

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An improved CS-LSSVM algorithm-based fault pattern recognition of ship power equipments.

Yifei Yang1,2, Minjia Tan2, Yuewei Dai1,2

  • 1School of Automation, Nanjing University of Science and Technology, Nanjing, Jiangsu, China.

Plos One
|February 10, 2017
PubMed
Summary

This study introduces an improved Cuckoo Search (CS) algorithm to optimize least squares support vector machine (LSSVM) parameters for ship power equipment fault identification with limited data, enhancing accuracy and speed.

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Area of Science:

  • Marine Engineering
  • Artificial Intelligence
  • Signal Processing

Background:

  • Ship power equipment fault monitoring often involves non-linear data with few samples.
  • Accurate fault pattern identification is crucial for operational safety and efficiency.

Purpose of the Study:

  • To address challenges in fault identification for ship power equipment using small, non-linear datasets.
  • To optimize the parameters of least squares support vector machine (LSSVM) for improved fault recognition.

Main Methods:

  • Utilized least squares support vector machine (LSSVM) for fault pattern identification.
  • Developed an improved Cuckoo Search (CS) algorithm for LSSVM parameter optimization, incorporating a dynamic adaptive strategy.
  • Enhanced the CS algorithm to improve recognition probability and searching step length, overcoming local extremum and convergence issues.

Main Results:

  • The improved CS algorithm demonstrated superior performance in optimizing LSSVM parameters compared to standard CS.
  • The proposed CS-LSSVM algorithm achieved accurate and effective identification of fault pattern types in ship power equipment.
  • The dynamic adaptive strategy in the CS algorithm addressed slow searching speed and low calculation accuracy.

Conclusions:

  • The CS-LSSVM algorithm offers a robust and efficient solution for fault diagnosis in ship power systems with limited data.
  • The enhanced CS algorithm provides a reliable method for optimizing complex machine learning models in marine engineering applications.
  • This approach significantly improves the accuracy and speed of fault pattern identification, contributing to enhanced maritime safety.