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

Load-frequency control01:28

Load-frequency control

817
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...
817
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

729
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.
729
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

888
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
888
The Power Flow Problem and Solution01:26

The Power Flow Problem and Solution

1.1K
Power flow problem analysis is fundamental for determining real and reactive power flows in network components, such as transmission lines, transformers, and loads. The power system's single-line diagram provides data on the bus, transmission line, and transformer. Each bus k in the system is characterized by four key variables: voltage magnitude Vk​, phase angle δk​, real power Pk​, and reactive power Qk​. Two of these four variables are inputs, while the power...
1.1K
Transmission Line Design Considerations01:23

Transmission Line Design Considerations

771
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...
771
Control of Power Flow01:30

Control of Power Flow

791
There are several methods to control power flow in power systems:
791

You might also read

Related Articles

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

Sort by
Same author

A lightweight graph-enhanced deep learning framework for explainable cucumber leaf disease diagnosis.

Scientific reports·2026
Same author

Benchmarking hybrid CNN and transformer backbones with graph convolution networks (GCN) for flower growth-stage classification.

Scientific reports·2026
Same author

Deep ensemble of multi-head attention CNNs for histopathological image-based of lung and colon cancer diagnosis.

Digital health·2026
Same author

Attention guided convolutional neural network with explainable AI for papaya leaf disease detection in edge and drone agricultural systems.

Scientific reports·2025
Same author

A machine learning-based EEG signal analysis framework to enhance emotional state detection.

Cognitive neurodynamics·2025
Same author

Optimizing FBS 3D positions for sum rate maximization in downlink NOMA 6G network.

Scientific reports·2025
Same journal

Analysis of strength degradation of coal and rock masses and stability of mined areas under long term immersion environment.

PloS one·2026
Same journal

Biogenic Silver-Selenium nanocomposite with anticancer activity and potent efficacy against vancomycin-resistant Staphylococcus aureus.

PloS one·2026
Same journal

Preparation and physicochemical characterization of a biodegradable chitosan/carboxymethyl cellulose hydrogel synthesized in NaOH/urea medium.

PloS one·2026
Same journal

Action-guilt, survivor-guilt, and depression in combat-related PTSD.

PloS one·2026
Same journal

Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability.

PloS one·2026
Same journal

Deep learning based two-way feature depiction model for brain tumor detection.

PloS one·2026
See all related articles

Related Experiment Videos

A novel LTE scheduling algorithm for green technology in smart grid.

Mohammad Nour Hindia1, Ahmed Wasif Reza1, Kamarul Ariffin Noordin1

  • 1Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia.

Plos One
|April 2, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for smart grid (SG) applications, optimizing bandwidth and scheduling for renewable energy systems. The proposed approach enhances performance for distribution automation (DA) and distributed energy resources (DER), improving overall efficiency.

Related Experiment Videos

Area of Science:

  • Electrical Engineering
  • Computer Science
  • Telecommunications

Background:

  • Smart grids (SG) are crucial for managing increasing power demands and integrating renewable energy sources.
  • Bidirectional communication is essential for effective SG operation, linking renewable energy systems with the electrical grid.
  • Existing scheduling algorithms have limitations in efficiently managing diverse SG application requirements within Long Term Evolution (LTE) networks.

Purpose of the Study:

  • To investigate the suitability of key smart grid applications—distribution automation (DA), distributed energy system-storage (DER), and electrical vehicle (EV)—within Long Term Evolution (LTE) networks.
  • To develop and evaluate a novel bandwidth estimation and allocation technique and a new scheduling algorithm to address weaknesses in current methods.
  • To enhance Quality of Service (QoS) for SG applications by prioritizing resources and making dynamic scheduling decisions.

Main Methods:

  • A novel bandwidth estimation and allocation technique prioritizing applications based on their needs.
  • A new scheduling algorithm utilizing dynamic weighting factors for multi-criteria decision-making.
  • Simulation of smart grid applications (DA, DER, EV) within an LTE network environment to test the proposed mechanism.

Main Results:

  • The proposed mechanism significantly improves throughput and reduces delay and packet loss for distribution automation (DA) and distributed energy resources (DER).
  • A satisfactory degree of service was achieved for electrical vehicle (EV) applications.
  • The new algorithm demonstrated superior fairness, outperforming existing algorithms (EXP-Rule, M-LWDF, EXP/PF) by 3%, 7%, and 9%, respectively.

Conclusions:

  • The developed bandwidth estimation, allocation, and scheduling techniques are effective for optimizing smart grid applications over LTE networks.
  • The proposed approach enhances overall network performance, particularly for critical applications like DA and DER, while ensuring a degree of service for EV.
  • The improved fairness indicates a more equitable distribution of network resources among competing smart grid services.