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The Swing Equation is a fundamental tool in power system dynamics, especially for analyzing the behavior of generating units like three-phase synchronous generators. This equation emerges from applying Newton's second law to the rotor of a generator, encompassing factors such as inertia, angular acceleration, and the interplay between mechanical and electrical torques.
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Updated: Jun 30, 2025

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Facilitating interaction between partial differential equation-based dynamics and unknown dynamics for regional wind

Shidong Chen1, Baoquan Zhang1, Xutao Li1

  • 1School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, 518055, Guangdong, China.

Neural Networks : the Official Journal of the International Neural Network Society
|March 20, 2024
PubMed
Summary

This study introduces a novel PDE-assisted network (PaNet) for regional wind speed prediction. PaNet integrates physical principles described by partial differential equations (PDEs) with data-driven dynamics, outperforming existing methods.

Keywords:
Deep learningPhysics informed neural networkRegional wind speed prediction

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Area of Science:

  • Environmental science
  • Computational fluid dynamics
  • Renewable energy systems

Background:

  • Regional wind speed prediction is vital for optimizing wind power utilization.
  • Complex wind dynamics present significant challenges for accurate prediction.
  • Underlying physical principles governing wind dynamics can be described by partial differential equations (PDEs).

Purpose of the Study:

  • To propose a novel approach, the PDE-assisted network (PaNet), for regional wind speed prediction.
  • To develop an architecture that integrates both PDE-based and unknown dynamics.
  • To enhance the accuracy and reliability of wind speed forecasting.

Main Methods:

  • A novel neural network architecture, PaNet, is devised, incorporating both PDE-based dynamics and unknown dynamics.
  • An inter-dynamics communication unit with attention gates regulates interactions between the two dynamics.
  • An adaptive frequency-gated unit generates a suitable initial state for PDE dynamics by selecting essential frequency components.

Main Results:

  • PaNet demonstrated superior predictive performance compared to baseline methods in comprehensive experiments.
  • The integration of PDE dynamics and attention-gated communication improved prediction accuracy.
  • The adaptive frequency-gated unit effectively optimized the initial state for PDE dynamics.

Conclusions:

  • The proposed PaNet offers a significant advancement in regional wind speed prediction.
  • Integrating physical principles (PDEs) with data-driven approaches enhances forecasting capabilities.
  • PaNet provides a robust framework for optimizing wind power utilization through accurate predictions.