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Related Concept Videos

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In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
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Updated: Oct 15, 2025

Measurements of Waves in a Wind-wave Tank Under Steady and Time-varying Wind Forcing
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Forecasting wind power ramps with prediction coordinates.

Yoshito Hirata1, José M Amigó2, Shunsuke Horai1

  • 1Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan.

Chaos (Woodbury, N.Y.)
|October 31, 2021
PubMed
Summary
This summary is machine-generated.

The prediction coordinates method accurately forecasts wind power ramps by analyzing nonlinear, stochastic system dynamics. This approach offers a reliable tool for renewable energy power output transitions.

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Area of Science:

  • Nonlinear time series analysis
  • Renewable energy systems
  • Stochastic dynamics

Background:

  • Wind power output is nonlinear and stochastic, similar to weather patterns.
  • Rapid transitions in wind power (ramps) can negatively impact electricity supply quality.

Purpose of the Study:

  • To apply the prediction coordinates method for forecasting wind power ramps.
  • To evaluate the effectiveness of this method using simulations and empirical data.

Main Methods:

  • Utilizing the prediction coordinates method, which explicitly incorporates dynamical noise.
  • Generating multiple predictions to infer system states and noise.
  • Testing the method with numerical simulations and real-world wind power data.

Main Results:

  • The prediction coordinates method demonstrated favorable performance compared to existing forecasting techniques.
  • The method proved effective in forecasting wind power ramps.

Conclusions:

  • The prediction coordinates method is a validated and reliable tool for forecasting transitions in nonlinear stochastic systems.
  • This method shows particular promise for applications in the renewable energy sector.