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

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:
Relation between Mathematical Equations and Block Diagrams01:20

Relation between Mathematical Equations and Block Diagrams

In a spring-mass-damper system, the second-order differential equation describes the dynamic behavior of the system. When transformed into the Laplace domain under zero initial conditions, this equation can be effectively analyzed and manipulated. The transformation into the Laplace domain converts differential equations into algebraic equations, simplifying the process of isolating the output.
Block Diagram Reduction01:22

Block Diagram Reduction

The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system.
Bus Impedance Matrix01:24

Bus Impedance Matrix

Calculating subtransient fault currents for three-phase faults in an N-bus power system involves using the positive-sequence network. When a three-phase short circuit occurs at a specific bus, the analysis uses the superposition method to evaluate two separate circuits.
In the first circuit, all machine voltage sources are short-circuited, leaving only the prefault voltage source at the fault location. The positive-sequence bus impedance matrix can be determined by solving the nodal equations,...

You might also read

Related Articles

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

Sort by
Same author

Multi-scaling reservoir computing learns noise-induced transitions with Lévy noise.

Chaos (Woodbury, N.Y.)·2025
Same author

Early warnings are too late when parameters change rapidly.

Scientific reports·2025
Same author

Nonmonotonic emergence of order from chaos in turbulent thermoacoustic fluid systems.

Physical review. E·2025
Same author

Dynamic Evolution of Complex Networks: A Reinforcement Learning Approach Applying Evolutionary Games to Community Structure.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

The circular movement of synchronous extreme precipitation preceding Kerala floods in 2018 and 2019.

Chaos (Woodbury, N.Y.)·2025
Same author

Extreme events in two coupled chaotic oscillators.

Physical review. E·2025

Related Experiment Video

Updated: Jun 21, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Matrix-measure criterion for synchronization in coupled-map networks.

Ping Li1, Maoyin Chen, Ye Wu

  • 1University of Electronic Science and Technology of China, Chengdu 610054, People's Republic of China.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|August 8, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces new criteria for synchronization in coupled-map networks. These conditions are more flexible, not requiring knowledge of the synchronous state or network connections.

More Related Videos

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study
04:44

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Published on: July 21, 2021

Super-Resolution Imaging to Study Co-Localization of Proteins and Synaptic Markers in Primary Neurons
14:02

Super-Resolution Imaging to Study Co-Localization of Proteins and Synaptic Markers in Primary Neurons

Published on: October 31, 2020

Related Experiment Videos

Last Updated: Jun 21, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study
04:44

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Published on: July 21, 2021

Super-Resolution Imaging to Study Co-Localization of Proteins and Synaptic Markers in Primary Neurons
14:02

Super-Resolution Imaging to Study Co-Localization of Proteins and Synaptic Markers in Primary Neurons

Published on: October 31, 2020

Area of Science:

  • Complex Systems
  • Network Dynamics
  • Nonlinear Dynamics

Background:

  • Synchronization is a key phenomenon in complex systems.
  • Existing synchronization conditions for coupled-map networks often have limitations.
  • Understanding synchronization is crucial for various applications.

Purpose of the Study:

  • To develop novel conditions for local and global synchronization in coupled-map networks.
  • To overcome limitations of existing synchronization criteria.
  • To provide a more general framework for analyzing synchronization in networks.

Main Methods:

  • Utilizing the matrix measure approach for analyzing synchronization.
  • Developing synchronization criteria independent of the synchronous state solution.
  • Investigating network connection limitations.

Main Results:

  • Established new, less restrictive conditions for synchronization.
  • Demonstrated that the criteria do not depend on the synchronous state.
  • Numerical simulations confirmed the effectiveness of the proposed conditions.

Conclusions:

  • The matrix measure approach provides powerful tools for synchronization analysis.
  • The new criteria offer greater flexibility in studying coupled-map networks.
  • The findings have implications for understanding and controlling synchronized behavior in complex systems.