Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Power System Three-Phase Short Circuits01:21

Power System Three-Phase Short Circuits

599
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...
599
Three-Phase Short Circuit—Unloaded Synchronous Machine01:21

Three-Phase Short Circuit—Unloaded Synchronous Machine

774
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...
774
Fault Types01:18

Fault Types

473
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...
473
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

412
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
412
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,...
551
Series R—L Circuit Transients01:22

Series R—L Circuit Transients

458
In a series resistor-inductor (R-L) circuit, closing the switch at the start of the time period simulates a three-phase short circuit, a fault condition where all three phases of an unloaded synchronous machine are short-circuited. When there is no fault impedance and no initial current, the initial voltage is determined by the phase angle of the source voltage.
Using Kirchhoff's Voltage Law (KVL) to analyze this circuit helps determine the total asymmetrical fault current, which consists...
458

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Adaptive Multioutput Gradient RBF Tracker for Nonlinear and Nonstationary Regression.

IEEE transactions on cybernetics·2023
Same author

Deep Cascade Gradient RBF Networks With Output-Relevant Feature Extraction and Adaptation for Nonlinear and Nonstationary Processes.

IEEE transactions on cybernetics·2022
Same author

Multi-Output Selective Ensemble Identification of Nonlinear and Nonstationary Industrial Processes.

IEEE transactions on neural networks and learning systems·2020
Same author

Characterization of extended-spectrum β-lactamases (ESBLs)-producing Salmonella in retail raw chicken carcasses.

International journal of food microbiology·2017
Same author

Sclareolide enhances gemcitabine‑induced cell death through mediating the NICD and Gli1 pathways in gemcitabine‑resistant human pancreatic cancer.

Molecular medicine reports·2017
Same author

Improving the reversibility of thermal denaturation and catalytic efficiency of Bacillus licheniformis α-amylase through stabilizing a long loop in domain B.

PloS one·2017
Same journal

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

IEEE transactions on neural networks and learning systems·2026
Same journal

CAFF-CIL: Causality-Aware Freedom Forgetting Approach for Class-Incremental Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Harmonic Autoencoding Framework for Multiple Tasks in Magnetic Particle Imaging Reconstruction.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Survey on Human-Centric Voice-Face Multimodal Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Vision-Assisted Foundation Model for Solving Multitask Vehicle Routing Problems.

IEEE transactions on neural networks and learning systems·2026
Same journal

FP3O: Enabling Proximal Policy Optimization in Multiagent Cooperation With Parameter-Sharing Versatility.

IEEE transactions on neural networks and learning systems·2026
See all related articles

Related Experiment Video

Updated: Mar 9, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.4K

Nonlinear Process Fault Diagnosis Based on Serial Principal Component Analysis.

Xiaogang Deng, Xuemin Tian, Sheng Chen

    IEEE Transactions on Neural Networks and Learning Systems
    |December 28, 2016
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces Serial Principal Component Analysis (SPCA), a hybrid method for industrial process monitoring. SPCA effectively detects and identifies faults in nonlinear processes by combining linear and nonlinear feature extraction.

    More Related Videos

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

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

    Published on: October 28, 2022

    2.2K
    Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
    06:22

    Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

    Published on: September 19, 2025

    597

    Related Experiment Videos

    Last Updated: Mar 9, 2026

    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
    14:27

    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

    Published on: June 26, 2013

    16.4K
    Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
    06:45

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

    Published on: October 28, 2022

    2.2K
    Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
    06:22

    Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

    Published on: September 19, 2025

    597

    Area of Science:

    • Chemical Engineering
    • Process Control
    • Data Science

    Background:

    • Industrial processes often exhibit both linear and nonlinear dynamics.
    • Kernel Principal Component Analysis (KPCA) is used for nonlinear process monitoring but may not be optimal for hybrid systems.
    • Existing methods may struggle to effectively address processes with both linear and nonlinear characteristics.

    Purpose of the Study:

    • To propose a novel hybrid linear-nonlinear statistical modeling approach for enhanced nonlinear process monitoring.
    • To develop a method that integrates linear and nonlinear feature extraction for improved fault detection and identification.
    • To address the limitations of traditional methods like KPCA in complex industrial settings.

    Main Methods:

    • A serial Principal Component Analysis (SPCA) approach is proposed, integrating linear Principal Component Analysis (PCA) and nonlinear KPCA.
    • PCA is first applied to extract linear features and decompose data into PC and residual subspaces.
    • KPCA is then applied to the residual subspace to extract nonlinear features, creating a hybrid model.

    Main Results:

    • Two monitoring statistics are developed for fault detection using both linear and nonlinear features from SPCA.
    • An SPCA similarity factor method is introduced for fault recognition, fusing linear and nonlinear features.
    • Case studies on a simulated nonlinear process and the Tennessee Eastman process validate the effectiveness of SPCA.

    Conclusions:

    • The proposed SPCA method simultaneously considers both linear and nonlinear features, outperforming KPCA alone in fault detection and identification.
    • SPCA offers a more effective approach for monitoring and diagnosing faults in industrial processes with mixed linear-nonlinear dynamics.
    • This hybrid strategy enhances the exploitation of underlying process structures for superior fault diagnosis performance.