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Fault Types01:18

Fault Types

147
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...
147
State Space Representation01:27

State Space Representation

324
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
324
Three-Phase Short Circuit—Unloaded Synchronous Machine01:21

Three-Phase Short Circuit—Unloaded Synchronous Machine

266
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...
266
Root Loci for Positive-Feedback Systems01:23

Root Loci for Positive-Feedback Systems

169
The Hartley oscillator is a positive feedback system that sustains oscillations by feeding the output back to the input in phase, thereby reinforcing the signal. Positive feedback systems can be viewed as negative feedback systems with inverted feedback signals. In these systems, the root locus encompasses all points on the s-plane where the angle of the system transfer function equals 360 degrees.
The construction rules for the root locus in positive feedback systems are similar to those in...
169
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

197
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...
197
Power System Three-Phase Short Circuits01:21

Power System Three-Phase Short Circuits

172
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...
172

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Related Experiment Video

Updated: Oct 10, 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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Fuzzy extended state observer for the fault detection and identification.

Pablo J Prieto1, Corina Plata-Ante2, Ramón Ramírez-Villalobos2

  • 1CETYS Universidad, Av. CETYS Universidad No. 4, Fracc. El Lago, 22210, B.C., México.

ISA Transactions
|December 10, 2021
PubMed
Summary

This study presents a new fuzzy extended system observer (FESO) for fault detection in autonomous nonlinear systems. The FESO method ensures system stability and accurately estimates faults using fuzzy logic.

Keywords:
Extended state observerFault detection and identificationLyapunov stabilityMamdani-type fuzzy system

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Area of Science:

  • Control Systems Engineering
  • Nonlinear System Analysis
  • Fuzzy Logic Applications

Background:

  • Autonomous nonlinear systems are susceptible to faults that can compromise their performance and safety.
  • Existing fault detection methods may lack robustness or require complex models.
  • Accurate fault estimation is crucial for effective fault-tolerant control.

Purpose of the Study:

  • To develop and validate a novel fault detection and identification methodology for autonomous nonlinear systems.
  • To design a fuzzy extended system observer (FESO) capable of estimating system faults.
  • To ensure the stability and boundedness of the proposed observer under Lyapunov criteria.

Main Methods:

  • Implementation of a Mamdani-type fuzzy system within an extended system observer framework.
  • Utilizing observer error as the fuzzy input variable for a single-input single-output (SISO) fuzzy system.
  • Employing Lyapunov stability analysis to verify the ultimate boundedness of the FESO solutions.

Main Results:

  • The proposed fuzzy extended system observer (FESO) effectively estimates faults in autonomous nonlinear systems.
  • Stability analysis confirms that the FESO solutions are ultimately bounded, ensuring reliable operation.
  • Simulation examples demonstrate the practical feasibility and accuracy of the FESO methodology.

Conclusions:

  • The developed FESO provides a robust and effective approach for fault detection and identification in nonlinear systems.
  • The fuzzy logic-based observer offers advantages in handling system uncertainties and nonlinearities.
  • The methodology is validated through simulations, confirming its potential for real-world applications.