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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.
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Universal framework for reconstructing complex networks and node dynamics from discrete or continuous dynamics data.

Yan Zhang1, Yu Guo2, Zhang Zhang1

  • 1School of Systems Science, Beijing Normal University, Beijing 100875, China.

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Summary

This study introduces a novel framework for simultaneously inferring complex system network structures and node dynamics from time-series data. The method accurately reconstructs interactions and behaviors, proving robust against noise and missing data.

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Area of Science:

  • Complex Systems Science
  • Network Science
  • Data Science

Background:

  • Dynamical processes in complex systems involve interacting nodes on a network.
  • Inferring both network structure and node dynamics from time-series data is challenging.
  • Existing methods often address network inference or dynamics reconstruction separately.

Purpose of the Study:

  • To develop a universal framework for simultaneous reconstruction of network structure and node dynamics.
  • To address limitations of conventional methods that focus on either structure or dynamics alone.

Main Methods:

  • A differentiable Bernoulli sampling process generates candidate network structures.
  • Neural networks simulate node dynamics based on the candidate network.
  • Stochastic gradient descent optimizes parameters to maximize data likelihood.

Main Results:

  • The proposed framework accurately recovers diverse network structures and node dynamics simultaneously.
  • The model demonstrates high accuracy in reconstruction tasks.
  • The method is effective across binary, discrete, and continuous time-series data.

Conclusions:

  • The universal framework offers a robust solution for joint network structure and dynamics inference.
  • The approach is resilient to noise and missing information in time-series data.
  • This work advances the analysis of complex systems by integrating structure and dynamics reconstruction.