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

The Power Flow Problem and Solution01:26

The Power Flow Problem and Solution

837
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 flow program computes...
837
Power System Three-Phase Short Circuits01:21

Power System Three-Phase Short Circuits

524
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...
524
Plane Potential Flows01:23

Plane Potential Flows

877
Plane potential flows simplify fluid motion by assuming the fluid to be irrotational and incompressible. These characteristics allow these flows to be described by a velocity potential function, ϕ, representing the flow speed in a given direction, and a stream function, ψ, that visualizes the flow path, both governed by Laplace's equation. These parameters help in estimating flow patterns, velocity distributions, and pressure fields around various hydraulic structures.
Uniform...
877
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
Node Analysis for AC Circuits01:14

Node Analysis for AC Circuits

636
Consider an angioplasty system featuring a catheter equipped with a turbine, a critical tool for removing plaque deposits from coronary arteries. This intricate medical device operates using a circuit model reminiscent of a dual-node RLC circuit powered by a current-controlled voltage source.
To unravel the complexities of this system, nodal analysis is employed, a powerful technique founded on Kirchhoff's current law (KCL), which remains valid for phasors. AC circuits can effectively be...
636
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

726
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:
726

You might also read

Related Articles

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

Sort by
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026

Related Experiment Video

Updated: Jan 16, 2026

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

11.1K

3V-GM: A Tri-Layer "Point-Line-Plane" Critical Node Identification Algorithm for New Power Systems.

Yuzhuo Dai1, Min Zhao1, Gengchen Zhang1

  • 1Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing 100876, China.

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

This study introduces a novel Three-Dimensional Value-Based Gravity Model (3V-GM) to identify critical nodes in power grids. The 3V-GM enhances grid stability assessment by integrating topology and electrical attributes for more accurate node identification.

Keywords:
MATPOWERcritical nodenew power systemthree-dimensional gravity modeltopological and electrical feature fusion

More Related Videos

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.6K
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

43.4K

Related Experiment Videos

Last Updated: Jan 16, 2026

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

11.1K
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.6K
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

43.4K

Area of Science:

  • Electrical Engineering
  • Power Systems Analysis
  • Network Science

Background:

  • The integration of renewable energy sources introduces intermittency and stochasticity, challenging power grid stability.
  • Traditional methods for identifying critical nodes often rely on incomplete network topology or power flow data, leading to inaccuracies.
  • Accurate identification of critical nodes is essential for maintaining grid reliability and operational security.

Purpose of the Study:

  • To develop a comprehensive model for identifying critical nodes in power grids that overcomes the limitations of existing methods.
  • To improve the accuracy and completeness of critical node identification by integrating structural and electrical-physical attributes.
  • To enhance the robustness and stability assessment of power grids with increasing renewable energy penetration.

Main Methods:

  • The Three-Dimensional Value-Based Gravity Model (3V-GM) was proposed, integrating node topology, real-time voltage state, electrical coupling distance, and eigenvector centrality.
  • Simulations were conducted on the IEEE 39 system and six other benchmark networks using Python and MATPOWER v7.1.
  • Node criticality was evaluated by measuring the load loss rate upon sequential node removal, comparing 3V-GM against six baseline methods.
  • Ablation experiments were performed to validate the contribution of each layer within the 3V-GM.

Main Results:

  • The 3V-GM consistently identified nodes whose removal resulted in significantly higher load loss rates compared to baseline methods across all tested networks.
  • The model demonstrated superior accuracy and stability in identifying critical nodes, crucial for grid operational planning.
  • Ablation studies confirmed the synergistic contribution of the plane, line, and point layers to the model's overall effectiveness.

Conclusions:

  • The 3V-GM offers a more accurate and comprehensive approach to critical node identification in power systems compared to traditional methods.
  • This enhanced identification capability is vital for improving the stability and resilience of power grids, especially with high renewable energy integration.
  • The model's multi-layered approach effectively captures complex network interdependencies, leading to better predictions of cascading failures.