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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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This study introduces an efficient event-driven simulation algorithm for multi-agent systems. The new method significantly reduces computational costs for modeling complex network dynamics and information spreading.

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Area of Science:

  • Complex Systems
  • Computational Science
  • Network Dynamics

Background:

  • Multi-agent systems with network topologies exhibit complex emergent behaviors.
  • Stochastic simulations (Monte-Carlo) are crucial for analyzing these systems but are computationally expensive.
  • High computational costs arise from updating agent interaction rates after each state transition.

Purpose of the Study:

  • To develop a computationally efficient simulation algorithm for continuous-time multi-agent systems.
  • To address the high computational burden of traditional stochastic simulations.
  • To enable accurate analysis of complex dynamical patterns in large and highly connected networks.

Main Methods:

  • Proposing a novel stochastic rejection-based, event-driven simulation algorithm.
  • Designing an algorithm that efficiently handles arbitrary probability densities for state transitions.
  • Ensuring the algorithm scales effectively with network size and connectivity.

Main Results:

  • The developed algorithm demonstrates significant computational efficiency.
  • The method scales exceptionally well with increasing network size and connectivity.
  • Statistically correct samples are produced, validating the algorithm's accuracy.

Conclusions:

  • The proposed event-driven simulation algorithm offers a highly scalable and accurate solution for studying multi-agent systems.
  • This advancement significantly reduces computational costs, making complex network dynamics and information spreading models more accessible.
  • The method is effective across various information spreading models, highlighting its broad applicability.