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

Multimachine Stability01:25

Multimachine Stability

273
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:
273
Energy Losses in Transformers01:21

Energy Losses in Transformers

1.1K
In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
There are four main reasons for energy losses in transformers.
The first cause can be  the high resistance of the...
1.1K
Power in a Three-Phase Circuit01:15

Power in a Three-Phase Circuit

487
Three-phase systems have two configurations: the wye and delta. A star configuration can be three or four wires; in a delta configuration, the components are connected in a closed loop. Instantaneous power refers to the power value at a precise moment, and in a balanced three-phase system, it is constant. This is because the sum of the instantaneous powers in the three phases remains steady over time, despite individual fluctuations, due to the symmetry and phase relationship. The total...
487
Energy and Power Signals01:17

Energy and Power Signals

825
In an electrical system with a resistor, voltage and current signals facilitate the measurement of power and energy across the resistor. For a continuous-time signal, the total energy over a time interval is defined as the integral of the square of the signal's magnitude over that interval. Mathematically, this is expressed as:
825
Energy Stored in Inductors01:16

Energy Stored in Inductors

676
An inductor is ingeniously crafted to accumulate energy within its magnetic field. This field is a direct result of the current that meanders through its coiled structure. When this current maintains a steady state, there is no detectable voltage across the inductor, prompting it to mimic the behavior of a short circuit when faced with direct current.
In terms of gauging the energy stored within an inductor, it is equivalent to the integral of the power delivered at every individual moment, all...
676
Power System Three-Phase Short Circuits01:21

Power System Three-Phase Short Circuits

308
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...
308

You might also read

Related Articles

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

Sort by
Same author

Synapse-inspired energy networks: a neuromorphic approach to microgrid protection without communication links.

Communications engineering·2026
Same author

Digital Twin Approach for Fault Diagnosis in Photovoltaic Plant DC-DC Converters.

Sensors (Basel, Switzerland)·2025
Same author

AI-aided power electronic converters automatic online real-time efficiency optimization method.

Fundamental research·2025
Same author

Big data analytics and artificial intelligence aspects for privacy and security concerns for demand response modelling in smart grid: A futuristic approach.

Heliyon·2024
Same author

Resource management with kernel-based approaches for grid-connected solar photovoltaic systems.

Heliyon·2022
Same author

Correlation-driven machine learning for accelerated reliability assessment of solder joints in electronics.

Scientific reports·2020
Same journal

Turbulent flow in a vortex separator with a directed pipe inlet.

Scientific reports·2026
Same journal

Systematic characteristic evaluation of clay-based cementitious material derived from calcium carbide residue and waste tile powder.

Scientific reports·2026
Same journal

Retraction Note: Improvement of a rapid diagnostic application of monoclonal antibodies against avian influenza H7 subtype virus using Europium nanoparticles.

Scientific reports·2026
Same journal

Applying large language models to spam detection in the Kazakh low-resource language setting.

Scientific reports·2026
Same journal

An open-source 3D printing system enabling in-situ freeze-thaw processing of hydrogels.

Scientific reports·2026
Same journal

An enhanced EfficientNet framework for automated waste classification using cosine annealing and label smoothing.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: Nov 10, 2025

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

3.3K

Intelligent long-term performance analysis in power electronics systems.

Saeed Peyghami1, Tomislav Dragicevic2, Frede Blaabjerg3

  • 1Department of Energy Technology, Aalborg University, 9220, Aalborg, Denmark. sap@et.aau.dk.

Scientific Reports
|April 7, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces constant lifetime curves, developed using Artificial Neural Networks (ANN), to predict power electronic converter reliability. This method offers a faster alternative to complex electro-thermal analysis for long-term performance assessment.

More Related Videos

Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment
04:35

Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment

Published on: July 5, 2024

2.2K
The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

11.9K

Related Experiment Videos

Last Updated: Nov 10, 2025

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

3.3K
Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment
04:35

Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment

Published on: July 5, 2024

2.2K
The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

11.9K

Area of Science:

  • Electrical Engineering
  • Power Electronics
  • Reliability Engineering

Background:

  • Traditional reliability modeling for power electronic converters relies on detailed component-level electro-thermal analysis, which is time-consuming.
  • Accurate long-term performance prediction is crucial for system design, operation, and maintenance planning in power electronic systems.

Purpose of the Study:

  • To propose a novel, long-term performance indicator for power electronic converters based on reliability.
  • To develop a computationally efficient surrogate model for predicting converter lifetime under various operating conditions.

Main Methods:

  • Utilized Artificial Neural Networks (ANN) to develop constant lifetime curves representing converter reliability.
  • Created a nonparametric surrogate model using limited non-linear data from theoretical reliability analysis.
  • Validated the approach through numerical case studies.

Main Results:

  • The proposed ANN-based lifetime curves effectively represent converter reliability under different operating conditions.
  • The surrogate model enables rapid prediction of converter lifetime, bypassing extensive electro-thermal simulations.
  • The lifetime curves facilitate system-level design optimization for reliability.

Conclusions:

  • The proposed reliability modeling approach provides an effective and efficient method for assessing long-term performance of power electronic converters.
  • This approach supports improved reliability, operation, and maintenance planning in power electronic systems.