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

Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

1.1K
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
1.1K
Multimachine Stability01:25

Multimachine Stability

545
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:
545
Distributed Loads01:19

Distributed Loads

947
Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
947
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

497
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
497
Load-frequency control01:28

Load-frequency control

623
Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
623
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

591
The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
591

You might also read

Related Articles

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

Sort by
Same author

Genome-Wide Analysis of the <i>FAR1</i>/<i>FHY3</i> (<i>FRS</i>) Gene Family and Expression Responses of <i>PbFRS</i> Genes to PEG-Induced Osmotic Stress, Light, and Shade in <i>Phoebe bournei</i>.

International journal of molecular sciences·2026
Same author

Power Restoration Optimization Strategy for Active Distribution Networks Using Improved Genetic Algorithm.

Biomimetics (Basel, Switzerland)·2025
Same author

Genome-Wide Characterization and Functional Analysis of <i>CsDOF</i> Transcription Factors in <i>Camellia sinensis</i> cv. Tieguanyin Under Combined Heat-Drought Stress.

Plants (Basel, Switzerland)·2025
Same author

Self-organizing feature selection fuzzy neural network-based terminal sliding mode control for uncertain nonlinear systems.

ISA transactions·2024
Same author

Adaptive Global Sliding-Mode Control for Dynamic Systems Using Double Hidden Layer Recurrent Neural Network Structure.

IEEE transactions on neural networks and learning systems·2019

Related Experiment Videos

Fault Recovery Strategy with Net Load Forecasting Using Bayesian Optimized LSTM for Distribution Networks.

Zekai Ding1, Yundi Chu1

  • 1College of Artificial Intelligence and Automation, Hohai University, Nanjing 210024, China.

Entropy (Basel, Switzerland)
|September 27, 2025
PubMed
Summary

This study enhances power grid fault restoration by forecasting net load with a Bayesian-optimized LSTM neural network. The proposed strategy improves critical load recovery and reduces network losses during disruptions.

Keywords:
Bayesian optimizationGA-QPSOLSTMactive distribution networkfault recovery

Related Experiment Videos

Area of Science:

  • Electrical Engineering
  • Power Systems
  • Artificial Intelligence

Background:

  • Distributed energy resource (DER) volatility complicates traditional distribution network fault restoration.
  • Accurate net load forecasting is crucial for effective restoration strategies.

Purpose of the Study:

  • To develop an advanced fault restoration strategy for distribution networks considering DER volatility.
  • To improve the speed and efficiency of power restoration after faults.

Main Methods:

  • Utilizing a Bayesian-optimized long short-term memory (LSTM) neural network for precise net load prediction.
  • Implementing a fast restoration optimization model with objectives for critical load recovery, minimized switching, and reduced network losses.
  • Employing a hybrid genetic algorithm-quantum particle swarm optimization (GA-QPSO) for solving the optimization model.

Main Results:

  • Achieved R2 of 0.9569 and RMSE of 12.15 kW in net load forecasting.
  • Reduced network losses by 33.2% compared to existing methods.
  • Extended power supply duration from 60 to 120 minutes and improved load recovery from 72.7% to 75.8%.

Conclusions:

  • The proposed net load forecasting and restoration strategy effectively addresses DER volatility impacts.
  • The GA-QPSO optimization significantly enhances restoration accuracy and efficiency.
  • Demonstrated improvements in network loss reduction, supply duration, and load recovery capabilities.