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

Transmission Line Design Considerations01:23

Transmission Line Design Considerations

125
Aluminum has become the material of choice for overhead transmission lines, surpassing copper due to its abundance and cost-effectiveness. The most prevalent type is the aluminum conductor, steel-reinforced (ACSR), which combines aluminum strands around a steel core. Other variants include all-aluminum conductors (AAC), all-aluminum alloy conductors (AAAC), aluminum conductor alloy-reinforced (ACAR), and aluminum-clad steel conductors. Advanced designs, such as aluminum conductors with steel...
125
Transmission-Line Differential Equations01:26

Transmission-Line Differential Equations

237
Transmission lines are essential components of electrical power systems. They are characterized by the distributed nature of resistance (R), inductance (L), and capacitance (C) per unit length. To analyze these lines, differential equations are employed to model the variations in voltage and current along the line.
Line Section Model
A circuit representing a line section of length Δx helps in understanding the transmission line parameters. The voltage V(x) and current i(x) are measured...
237
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

551
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
551
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

95
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.
95
Ampere's Law: Problem-Solving01:31

Ampere's Law: Problem-Solving

3.5K
Ampere's law states that for any closed looped path, the line integral of the magnetic field along the path equals the vacuum permeability times the current enclosed in the loop. If the fingers of the right hand curl along the direction of the integration path, the current in the direction of the thumb is considered positive. The current opposite to the thumb direction is considered negative.
Specific steps need to be considered while calculating the symmetric magnetic field distribution...
3.5K
Reducing Line Loss01:18

Reducing Line Loss

144
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
144

You might also read

Related Articles

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

Sort by
Same journal

Α new mixed δ-shock model with a change in shock distribution.

Top (Berlin, Germany)·2024
Same journal

Mathematical optimization in classification and regression trees.

Top (Berlin, Germany)·2024
Same journal

Mathematical optimization models for reallocating and sharing health equipment in pandemic situations.

Top (Berlin, Germany)·2023
Same journal

Cutting uncertain stock and vehicle routing in a sustainability forestry harvesting problem.

Top (Berlin, Germany)·2023
See all related articles

Related Experiment Video

Updated: Jun 9, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

490

Learning-assisted optimization for transmission switching.

Salvador Pineda1,2, Juan Miguel Morales3,2, Asunción Jiménez-Cordero3,2

  • 1Department of Electrical Engineering, University of Málaga, Málaga, Spain.

Top (Berlin, Germany)
|October 31, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning approach to solve the challenging Direct Current Optimal Transmission Switching (DC-OTS) problem in power systems. The method uses past solutions to accelerate optimization for finding cost-effective grid configurations.

Keywords:
Machine learningMathematical optimizationMixed-integer programmingOptimal power flowOptimal transmission switching

More Related Videos

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

9.8K
New Variations for Strategy Set-shifting in the Rat
09:45

New Variations for Strategy Set-shifting in the Rat

Published on: January 23, 2017

8.2K

Related Experiment Videos

Last Updated: Jun 9, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

490
A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

9.8K
New Variations for Strategy Set-shifting in the Rat
09:45

New Variations for Strategy Set-shifting in the Rat

Published on: January 23, 2017

8.2K

Area of Science:

  • Optimization
  • Machine Learning
  • Power Systems Engineering

Background:

  • The Direct Current Optimal Transmission Switching (DC-OTS) problem is crucial for optimizing power grid operations and maintaining generation-demand balance, especially with increasing grid variability.
  • DC-OTS is a complex, NP-hard mixed-integer programming problem due to binary variables determining transmission line status.

Purpose of the Study:

  • To propose a novel machine learning-based procedure to efficiently solve the computationally difficult DC-OTS problem.
  • To leverage historical problem instance solutions to accelerate the optimization of new, unseen DC-OTS models.

Main Methods:

  • Development of a learning procedure that utilizes solutions from previous DC-OTS instances.
  • Application of the proposed method to a real-life power system dataset for numerical experimentation.

Main Results:

  • The proposed approach demonstrates a very high success rate in identifying optimal power grid topologies.
  • Significant speed-up factors were achieved compared to alternative heuristic methods.
  • While optimality guarantees are not provided, the method offers practical efficiency.

Conclusions:

  • Machine learning offers a promising avenue for addressing complex optimization challenges in power systems.
  • The developed learning procedure effectively accelerates the solution of the DC-OTS problem, enhancing operational efficiency and cost-effectiveness in power grids.