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

Three-Phase Short Circuit—Unloaded Synchronous Machine01:21

Three-Phase Short Circuit—Unloaded Synchronous Machine

110
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...
110
Multimachine Stability01:25

Multimachine Stability

130
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:
130
Wind Turbine Machine Models01:24

Wind Turbine Machine Models

96
In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
Induction machines interact through the rotating magnetic field generated by the stator and the rotor. The key parameter is slip, which is the difference between synchronous speed and rotor speed relative to synchronous speed. Slip is...
96
Generator Voltage Control01:21

Generator Voltage Control

109
Generator voltage control is crucial for maintaining the stable operation of synchronous generators and wind turbines. In older models, a DC generator driven by the rotor delivers DC power to the rotor's field winding, and the power is transferred through slip rings and brushes. In the latest models, static or brushless exciters are used. Static exciters rectify AC power from the generator terminals and then transfer the DC power directly to the rotor. Brushless exciters, on the other hand,...
109
Turbine-Governor Control01:17

Turbine-Governor Control

146
Turbine-governor control is crucial for maintaining power system stability by balancing turbine mechanical power output with electrical load demand. This mechanism ensures that generator frequency and rotor speed are within acceptable limits during load variations. Turbine-generator units store kinetic energy due to their rotating masses; this energy is released to meet the load requirement when the load increases. The electrical torque of turbines rises to meet the demand, whereas the...
146
Design Example: Calculating Safe Diameter for Wind-Exposed Disc01:17

Design Example: Calculating Safe Diameter for Wind-Exposed Disc

17
Assessing safety in wind-exposed installations is crucial to preventing potential failures. This example explores the calculation and design adjustments needed to mount a circular disc on a building facade, where wind forces are a primary concern. A 4-meter diameter disc was initially designed as an aesthetic feature facing winds at a velocity of 25 meters per second, with an air density of 1.25 kilograms per cubic meter. Given these conditions, the drag force on the disc was determined using...
17

You might also read

Related Articles

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

Sort by
Same author

Remaining Useful Life Estimation of Hollow Worn Railway Vehicle Wheels via On-Board Random Vibration-Based Wheel Tread Depth Estimation.

Sensors (Basel, Switzerland)·2024
Same author

Vibration-Based SHM in the Synthetic Mooring Lines of the Semisubmersible OO-Star Wind Floater under Varying Environmental and Operational Conditions.

Sensors (Basel, Switzerland)·2024
See all related articles

Related Experiment Video

Updated: May 1, 2026

Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

5.6K

Machine Learning-Based Damage Diagnosis in Floating Wind Turbines Using Vibration Signals: A Lab-Scale Study Under

John S Korolis1, Dimitrios M Bourdalos1, John S Sakellariou1

  • 1Stochastic Mechanical Systems & Automation (SMSA) Laboratory, Department of Mechanical Engineering and Aeronautics, University of Patras, 26504 Patras, Greece.

Sensors (Basel, Switzerland)
|February 26, 2025
PubMed
Summary

Remote diagnosis using machine learning (ML) structural health monitoring (SHM) accurately detects, identifies, and assesses damage in floating wind turbines (FWTs). This advanced method ensures timely repairs, enhances safety, and reduces costs for offshore operations.

Keywords:
damage diagnosisfloating wind turbinemachine learning methodsvarying wind conditionsvibration signals

More Related Videos

Measurements of Waves in a Wind-wave Tank Under Steady and Time-varying Wind Forcing
08:54

Measurements of Waves in a Wind-wave Tank Under Steady and Time-varying Wind Forcing

Published on: February 13, 2018

8.1K
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

1.7K

Related Experiment Videos

Last Updated: May 1, 2026

Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

5.6K
Measurements of Waves in a Wind-wave Tank Under Steady and Time-varying Wind Forcing
08:54

Measurements of Waves in a Wind-wave Tank Under Steady and Time-varying Wind Forcing

Published on: February 13, 2018

8.1K
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

1.7K

Area of Science:

  • Engineering
  • Renewable Energy
  • Structural Health Monitoring

Background:

  • Offshore floating wind turbines (FWTs) face harsh conditions, making in situ monitoring dangerous and costly.
  • Early damage detection in FWTs is crucial for safety, operational lifecycle, and cost reduction.
  • Automated, remote diagnosis via vibration-based structural health monitoring (SHM) is essential for FWTs.

Purpose of the Study:

  • To investigate the complete damage diagnosis problem for FWTs using machine learning (ML) SHM.
  • To evaluate the effectiveness of ML SHM methods in detecting, identifying, and characterizing damage.
  • To assess the performance of ML SHM with limited sensor data.

Main Methods:

  • Conducted hundreds of experiments on a lab-scale FWT model.
  • Simulated various damage scenarios: blade cracks, added masses (ice accumulation), and connection degradation.
  • Employed well-established ML SHM methods with damage-sensitive feature vectors across the full frequency bandwidth.

Main Results:

  • Achieved 100% success in all damage diagnosis stages (detection, identification, severity).
  • Demonstrated flawless diagnosis even with a single vibration sensor.
  • Validated the effectiveness of ML SHM for comprehensive FWT condition assessment.

Conclusions:

  • Advanced ML SHM methods can reliably diagnose complete damage in FWTs.
  • The proposed approach enhances FWT safety and operational efficiency.
  • This study establishes a robust framework for remote, automated FWT structural health monitoring.